KAPITALISME & NEOLIBERALISME

Ekonomi Politik Journal Al-Manär Edisi I/2004
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KAPITALISME &
NEOLIBERALISME
Sebuah Tinjauan Singkat1
Eko Prasetyo
Keberatan terbesar kalangan mahasiswa terhadap pemotongan subsidi ditengarai
karena kebijakan yang ada di balik itu didasarkan pada kepatuhan atas ajaran yang tertuang
dalam ideologi neo-liberalisme. Yang terpokok dari ideologi neo-liberalisme adalah
dikarantinanya peran sosial negara dan menjadikan pasar bebas sebagai kiblat dari semua
transaksi ekonomi. Kedua kecenderungan ini membawa akibat serius bagi kehidupan
mayoritas rakyat yang masih berada dalam krisis. Segala kritik yang ditumpahkan oleh
sejumlah aktivis tidak mengurangi keyakinan penguasa untuk tetap menerapkan ideologi
neo-liberalisme dalam berbagai proyek pembangunan. Kerasnya suara perlawanan di
tingkat akar rumput ini telah memperluas wacana ideologi neo-liberalisme pada semua
komunitas masyarakat sipil. Aksi penentangan yang tidak percuma mengingat saat ini,
banyak kalangan mulai kembali menelaah, apa sesungguhnya yang ada di balik ideologi
neo-liberalisme dan bagaimana kiprahnya. Sejarah tentang pergerakan modal tak lagi
ditatap sebagai soal ekonomi semata akan tetapi juga ditinjau dari sudut politik, sosial
bahkan persentuhannya dengan keyakinan agama. Dinamika konflik antara modal dengan
negara saat ini menemukan babak baru dan melaluinya beberapa teori perubahan sosial
1 Disampaikan untuk PubDisc (Public Discussion) SCIENCES,19 April 2003
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Ekonomi Politik Journal Al-Manär Edisi I/2004
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kini dirumuskan.
Dalam paparannya, Anthony Giddens menyatakan kalau modernitas disangga oleh
kekuatan kapitalisme, negara bangsa, organisasi militer dan industrialisasi. Kapitalisme
merujuk pada sejumlah prinsip struktural yang mendasari praktik akumulasi modal dalam
konteks pasar produksi dan tenaga kerja yang kompetitif. Sedang negara-bangsa menunjuk
pada prinsip struktural yang mengoordinasi praktik kontrol atas informasi, supervisi sosial
dan pemata-mataan. Lalu militerisme menyangkut prinsip struktural yang mendasari
praktik pengontrolan atas alat-alat kekerasan dalam konteks industrialisasi perang.
Akhirnya industrialisme menyangkut prinsip struktural yang mendasari praktik-praktik
yang bertujuan untuk mengubah alam atau pembangunan lingkungan non alami2.
Keempatnya merupakan tulang punggung yang menghamba pada modernitas dan darinya
proses transformasi sosial masyarakat bekerja. Dalam konteks perbincangan kali ini,
kapitalisme kiranya menjadi sistem yang berkait-erat dengan proses berjalin-kelindanya
modal. Kapitalisme membawa dunia pada sistem perekonomian yang tunduk pada norma
serta aturan pasar. Terobosan kapitalisme adalah membentuk sistem pasar yang
hegemonik dimana kekuasaan privat juga memiliki kemampuan untuk mencipta pengaruh
pada kawasan publik. Mengapa kekuatan kapitalisme bisa sejauh itu dampaknya?
Adam Smith adalah peletak dasar pemikiran kapitalisme yang menjelaskan
bekerjanya mekanisme hukum pasar atas dasar dorongan kepentingan-kepentingan pribadi
karena kompetisi dan kekuatan individualisme dalam menciptakan keteraturan ekonomi3.
Melaluinya, kapitalisme melakukan klasifikasi antara nilai guna dengan nilai tukar yang ada
pada setiap komoditi. Ukuran riil dari nilai tukar komoditi, harus dilihat dari kondisi
pertukaran, dimana 'ukuran riil' dari nilai komoditi adalah kuantitas dari kerja yang berada
dalam barang-barang lain yang dapat dipertukarkan di pasar. Tokoh berikutnya yang
penting adalah David Ricardo, yang melakukan kritik terhadap Adam Smith, terutama
yang berkaitan dengan nilai komoditi. Menurutnya, nilai komoditi terdapat pada kerja
manusia berikut bahan-bahan mentah dan alat-alat kerja. Ricardo menemukan bahwa
2 Lih B Herry Priyono, Anthony Giddens, Suatu Pengantar, KPG, 2003
3 Kalimat yang populer dari Adam Smith "Bukanlah dari kemurahan hati tukang daging, tukang bir atau tukang rot/', kita mengharapkan
mendapat makanan; melainkan dari penghargaan mereka atas kepentingan din mereka masing-masing. Kita camkan dalam din kita, bahwa
bukanlah dari rasa kemanusiaan, melainkan dan rasa cinta terhadap diri-sendiri; dan tak akan kita berbicara pada mereka mengenai
kebutuhan-kebutuhan kita bersama, melainkan atas dasar laba yang bisa mereka rain' Lih Bonnie Setiawan, Peralihan Kapitalisme Di Dunia
Ketiga, Insist Press, 1999
Ekonomi Politik Journal Al-Manär Edisi I/2004
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komoditi yang dijual pada harganya, kira-kira akan setara dengan jumlah kerja yang
diperlukan untuk memproduksinnya. Asumsinya satu-satunya nilai tukar, berawal dari
jumlah kerja yang digunakan untuk memproduksi, Karenanya dari Ricardo-lah sifat parasit
dari seluruh pendapatan yang tidak diperoleh dari kerja terbongkar, sebab darinya, kelak
akan ditemukan apa yang dinamai dengan nilai lebih dan kerja lebih.
Kedua ilmuwan ini menjadi peletak dasar bagi ideologi kapitalisme awal dan
mereka hidup pada masa transisi dari ekonomi subsisten menuju pada sistem ekonomi
pasar, yang mengandalkan pada laba. Sejumlah ilmuwan kemudian memberikan
pendasaran historis tentang masa peralihan ke kapitalisme ini dengan ditandai oleh
sejumlah indikator: pertama meningkatnya output pertanian yang bersamaan dengan
pemisahan petani-petani dari tanahnya, kedua pertumbuhan produksi komoditi dan
pembagian kerja, ketiga akumulasi modal oleh pedagang dan petani kaya. Paul Baran
menyatakan bahwa kapitalisme terbentuk ketika terjadi akumulasi modal dalam bentuk
modal dagang yang kemudian menjadi dasar ekspansi Eropa dimana negara memberikan
dukungan terhadap kompetisi. Dengan demikian, Baran melihat perkembangan
kapitalisme sebagai perkembangan di satu wilayah dengan mengorbankan wilayah-wilayah
lainnya. Baran berjasa dalam meletakkan dasar-dasar eksploitasi kapitalisme yang
dilanjutkan oleh sejumlah teoritisi neo marxis yang menjelaskan tentang bagaimana
ekspansi kolonial ini telah membawa ketergantungan pada sejumlah negara. Ekspansi
kolonial ini juga memperkenalkan kemajuan dari organisasi militer, yang oleh Amartya
Sein, telah membawa pada dua tingkat; pertama karakter massal tuntutan militer telah
merangsang rasionalisasi proses produksi dan kedua tentara itu sendiri merupakan model
bagi organisasi industri dan organisasi sosial.
Tapi Perang Dunia II telah mendorong upaya untuk penyusunan kembali
pemikiran ekonomi yang kemudian melahirkan ekonomi pembangunan, Gunnar Myrdal
menyatakan gagasan pembangunan ini dilatarbelakangi oleh: pertama likuidasi kekuasaan
struktur kolonial yang cepat, kedua adanya harapan akan perkembangan di negara-negara
terbelakang itu sendiri, ketiga ketegangan internasional, yang memuncak pada perang
dingin, yang membuat nasib negara-negara terbelakang menjadi keprihatinan kebijakan
luar negeri.4 Pasca Perang Dunia II ini telah membawa upaya beberapa negara, terutama
Amerika, untuk memimpin proses rekonstruksi. Instrumen untuk ini ada dalam program
4 Lih Bjorn Hettne, Teori Pembangunan dan Tiga Dunia, Gramedia, 2001
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besar-besaran yang dinamai dengan Marshal Aid yang bertujuan ganda, pertama untuk
menjalankan ekonomi dunia (menurut sistem Bretton Woods) dan menahan laju
komunisme. Paling tidak, ada tiga pilar di balik pemunculan teori pembangunan, yakni;
pertumbuhan, perencanaan dan bantuan. Dalam pengertian Gramscian, tatanan dunia
pasca perang -yang memunculkan gagasan pembangunan- ini sangat hegemonik.
Mengingat, pertama secara gemilang AS berhasil mendifinisikan kepentingan korporasi
ekonominya dalam sebuah kerangka global dan bersedia memikul beban kepemimpinan.
Kedua kepemimpinan AS atas sekutu-sekutu Eropa tidak semata-mata dibangun di atas
dominasi ekonomi, politik atau keunggulan militer, tetapi lebih didasarkan pada
konvergensi kepentingan dan sikap elite di negara-negara itu, dan semakin meningkatnya
penerimaan visi internationalis liberal AS mengenai ekonomi dunia yang terbuka yang
dirancang menguntungkan para pesertanya, meski tidak seimbang5.
Tapi, proyek ini ternyata membawa kegagalan serius, sebagaimana yang dinyatakan
oleh berbagai kalangan, pertumbuhan dengan tanpa pembangunan tetapi dengan
kemiskinan. 1-1 Cheners menyatakan "sekarang jelas bahwa lebih dari satu dekade,
pertumbuhan yang pesat di negara-negara terbelakang menghasilkan sedikit sekali
keuntungan bagi sekitar sepertiga penduduknya". Yang lebih berbahaya dampak dari
adopsi kebijakan pembangunan adalah timbunan hutang yang ada di negara-negara
berkembang. Karenanya, diperlukan sebuah strategi baru, yang kemudian dikenal dengan
neo-liberalisme. Pada dasarnya neo-liberalisme adalah sebuah reaksi terhadap
membesarnya peran negara yang menyebabkan kehancuran sistem pasar. Jalan keluar yang
diusulkan oleh ideologi neo-liberalisme adalah melucuti peran negara dan mengembalikan
semua transaksi ekonomi ke dalam hukum pasar. Sehingga ketika Indonesia terjatuh pada
krisis, neo-liberalisme memberikan beberapa penjelasan tentang sebab-musabanya6.
Pertama krisis terjadi karena kebijakan makro yang diterapkan sehingga krisis dipandang
dalam konteks balance of payment (depresiasi uang, jatuhnya nilai tukar) kedua financial panic
yakni kepanikan nasabah Bank, ketiga Bubble Colaps atau model balon mengempis karena
prilaku para spekulator, keempat moral hazarrd cyrisis terhadap institusi perbankan dan
terakhir disoderly workout yakni kekacauan terjadi ketika peminjam tidak lancar
5 Lih Muhadi Sugiono, Kritik Antonio Gramsci Terhadap Pembangunan Dunia Ketiga, Pustaka Pelajar,
1999
6 Lih Mansour Fakih, Sesat Pikir Teori Pembangunan 6' Globalisasi, Insist Press dan Pustaka Pelajar, 2001
Ekonomi Politik Journal Al-Manär Edisi I/2004
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memprovokasi kreditor untuk berlomba dan memaksa likuiditas.
Itu sebabnya kehadiran IMF menjadi diperlukan terutama ketika banyak negara
tidak mampu membayar hutangnya kembali. Semula Meksiko yang gagal membayar
hutangnya yang jatuh tempo pada tahun 1982. IMF, pada saat Meksiko mengalami
masalah, diperlukan untuk membantu menyelamatkan neraca pembayaran dan mengatur
perundingan restrukturisasi utang dengan kalangan Perbankan International. Perannya
menjadi kian penting saat Asia memasuki krisis terberatnya pada dekade 1997 dimana
IMF mencoba ikut memecahkan. Salah satu program IMF yang populer dinamakan
dengan SAP {Structural Adjusment Program) yang didasarkan atas keyakinan bahwa sektor
swasta lebih efektif, dinamis dan bereaksi lebih baik terhadap ekonomi pasar daripada
sektor pemerintah. Karenanya IMF selalu mendorong setiap negara untuk berintegrasi
dalam pasar dunia melalui beberapa kebijakan, diantaranya7: pertama menurunkan nilai
tukar mata uangnya agar lebih kompetitif, kedua mengurangi hambatan-hambatan
perdagangan sehingga mendorong industri lokal lebih kompetitif dalam menghadapi
produk impor yang lebih murah, ketiga memberikan insentif ekspor seperti keringanan
pajak dan subsidi keuangan, keempat merangsang investasi asing dengan menciptakan
wilayah perdagangan bebas atau memberikan pembebasan pajak. Di samping sejumlah
program ini juga ada sejumlah bantuan yang berada di bawah program-program IMF yang
tetap konsisten dengan paradigma utamanya, yakni mencebur dalam mekanisme pasar
bebas.
Peran IMF yang terpenting adalah melakukan liberalisasi finansial dan ini
sepenuhnya mendapat dukungan penuh Amerika. Bill Clinton yang menetapkan ekonomi
sebagai fokus kebijakan luar negerinya membentuk Dewan Ekonomi Nasional yang
kedudukannya setara dengan Dewan Keamanan. Liberalisasi Finansial yang dipaksakan
pada semua negara tentu memiliki efek yang membahayakan. Apalagi ketika kebijakan
Liberalisasi Keuangan ini mendapat dukungan besar dari NATO, yang memiliki tujuan
untuk menyebar-luaskan keamanan dan stabilitas yang dinikmati Eropa Barat sejak Perang
Dunia II ke Eropa Tengah dan Timur. Penyebarluasan tersebut akan menciptakan
prospek yang bagus untuk menarik investasi. Bahkan Cohen menyatakan, strategi
pemerintah untuk menentang “kekerasan dan instabilitas-instabilitas yang membahayakan
7 Carol Welch, Panduan Mengenai IMF, INFID Jakarta
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nyawa manusia dan pasar”.8 Tentu kebijakan ini sudah tentu akan membawa dampak yang
muram, terutama ketika dikaitkan dengan pendapat yang dikemukakan pertama kali oleh,
John Maynard Keynes. Dikatakan, liberalisasi kapital akan merampas kemampuan negara
untuk melaksanakan kebijakan ekonomi yang independen. Keynes selalu menganggap
pasar itu sesungguhnya bersifat irasional. Tetapi, nampaknya Amerika bersikukuh untuk
tetap menyakini akan liberalisasi pasar. Dalam laporan sub-komite senat dikatakan, teologi
yang menggerakkan sistem ini adalah keyakinan tak tergoyahkan terhadap pergerakan
modal bebas tanpa batasan atau regulasi. Tujuan kebijakan AS adalah untuk memastikan
keamanan dan mobilitas modal. Sebuah keyakinan yang mesin utamanya adalah IMF dan
kekuasaan otoriter ini tentu memiliki, sejumlah kelemahan-kelemahan serius.
Tentu ada sejumlah kelemahan-kelemahan yang ada dalam IMF saat menjalankan
programnya. Kritikan utama yang selalu muncul adalah cara kerja IMF yang sangat
tertutup dan andaikan ada informasi maka itupun informasi yang sangat sepele. Kritik lain
adalah tidak adanya akuntabilitas dan evaluasi terhadap sejumlah program IMF. Apalagi
IMF selalu mengaku sebagai lembaga antar pemerintah sehingga tidak merasa perlu
bertanggung jawab kepada publik. Akuntabilitas dan evaluasi tidak terjadi karena IMF
selalu menghindar berurusan dengan wakil pemerintah dari kalangan yang lebih luas,
dengan berdalih pada artikel V statuta-nya, yang menyatakan bahwa kementrian keuangan
dan para pejabat Bank Sentral adalah pihak yang memiliki hubungan langsung dengan
IMF. Di sisi lain pendekatan IMF terhadap persoalan tenaga kerja benar-benar mengacu
pada pasar, fleksibilitas tenaga kerja akan memberi rangsangan bagi bisnis dan penanaman
modal yang pasti akan mendorong kenaikan upah maupun perubahan iklim kerja jika
negara terus berkembang. Dampak pendekatan ini yang menyolok adalah melejitnya angka
pengangguran. Selain itu juga yang tak kalah hebohnya, perhatian IMF pada perdagangan
bebas dan pertumbuhan ekonomi yang mengandalkan ekspor telah 'berhasil' merusak
lingkungan. Mengingat sejumlah kelemahan-kelemahan diatas itu pulalah maka ada kritik
bahkan tuntutan untuk membubarkan saja institusi ini.
Tuntutan yang makin mengeras ini telah mengetuk Washington untuk kembali
memikirkan strategi penaklukan sejumlah gerakan oposisi. Diantara taktik yang diterapkan
adalah9 (1) Washington berusaha memecah-belah oposisi anti diktator dengan mendanai
dan mengatur kelompok borjuis liberal sambil mengisolir dan mendemobilisasi gerakan-
8 William K Tabb, Tabir Politik Globalisasi, 2003, Lafadi, Yogyakarta
9Lih James Petras dan Heltmeyer, Imperialisme Abad 21, Kreasi Wacana, 2002
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gerakan kerakyatan sayap kiri (2) Washington mengkampanyekan transisi hasil negoisasi
antara liberal borjuis dan militer yang akan mempertahankan kekuatan bersenjata,
memperkuat kebijakan-kebijakan "pasar bebas" dan memperkenalkan pemilihan umum.
Kemunculan sejumlah ornop yang menggerakkan agenda demokratisasi sebenarnya
dilandasi oleh motif itu, karenanya beberapa kalangan mulai menggulirkan beberapa kritik,
yang berkisar pada; pertama ornop telah menjadi tempat berteduh yang nyaman bagi
sejumlah intelektual yang ingin 'bertahan hidup', kedua kegiatan ornop telah menjadi
komoditas yang berorientasi semata-mata pada proyek yang bisa 'dijual', ketiga ornop
menjadi lowongan kerja tersendiri yang memiliki potensi untuk menampung tenaga kerja.
Ringkasnya, gerakan ornop telah menjadi kekuatan proyek dan lama-kelamaan memang
tidak lagi berorientasi gerakan. Dalam kaitan inilah, proyek neo-liberalisme ditegakkan, di
tengah lesunya gerakan kerakyatan dan buasnya kekuatan swasta yang hendak
menggantikan kuasa dari pemerintah.
Dengan mempertimbangkan itu semua, kiranya ada fungsi dan mandat yang bisa
dilakukan oleh LDK (Lembaga Dakwah Kampus). Pertama yang teramat penting adalah
mendorong kesadaran kritis mengenai apa itu kapitalisme. LDK harus mampu untuk
menjelaskan dalam bahasa yang komunikatif pada publik mengenai apa itu kapitalisme,
mengingat ancaman yang dibawanya sekaligus korban yang berjatuhan akibat penerapan
ideologi ini. Kalau perlu 'motif’ penghancuran dari sistem ekonomi yang kapitalistik ini
dibaca dalam konteks semangat moral. Tujuannya sederhana, agar persoalan kapitalisme
ini tidak melulu dihadapi sebagai soal ekonomi melainkan juga pada tataran nilai. Kedua
tak kalah pentingnya adalah mulai merintis jaringan bukan lagi berdasarkan atas 'kesamaan
iman' saja melainkan juga atas basis kesamaan pada persoalan sosial. LDK perlu lebih
mengintensifkan hubungan dengan berbagai kekuatan anti kapitalisme yang mungkin
dapat menyediakan sejumlah data, informasi bahkan wacana mengenai kapitalisme ini.
Jaringan ini menjadi mudah saat ini, terutama dengan berkembang-luasnya gerakan anti
kapitalisme belakangan ini. Di samping itu yang tak kalah pentingnya adalah mengaktifkan
kembali kegiatan advokasi, yang tidak semata-mata dipandang sebagai kegiatan sekuler,
melainkan kegiatan pembelaan terhadap kaum yang dianiaya. Usaha untuk ini perlu
ditempuh mengingat krisis yang berpekepanjangan ini, tak lagi bisa dilihat sebagai
ancaman sosial melainkan juga ancaman akan runtuhnya nilai-nilai kemanusiaan.
Berangkat dari sana nampaknya, orientasi LDK yang selalu mendorong pembentukan
komunitas atau masyarakat yang berakhlak mulia perlu ditambah dengan mandat,
Ekonomi Politik Journal Al-Manär Edisi I/2004
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penciptaan masyarakat yang adil dan egaliter. Cita-cita ideal yang kini sedang dirusak oleh
sistem Kapitalisme maupun oleh sistem globalisasi.
*********
Ekonomi Politik Journal Al-Manär Edisi I/2004
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Eko Prasetyo, adalah alumnus Fakultas Hukum Ull tahun 1997, kemudian melanjutkan
studi S2 di fakultas dan umversitas yang sama, namun tidak selesai. Mengawali "karir"
dengan menjadi guru TPA di Kota Gede dan pernah jadi kepala sekolah TPA di kampung
Pujokusuman Yogyakarta. Pernah menjadi bagian dan kepanitiaan ramadhan di Masjid
Syuhada Kota baru dalam Training Keluarga Sakinah. Selain aktif di Insist Press, Pusham
Ull dan redaksi tetap Jurnal Wacana, sempat juga menjadi anggota Tim Pembela Muslim
untuk advokasi hukum Laskar Jihad dan pernah menulis beberapa artikel untuk tabloid
Laskar Jihad. Beberapa tulisannya dalam bentuk buku telah diterbitkan antara lain: HAM:
Kejahatan Negara dan Imperialisme Modal (2001), Islam Kiri Melawan Kapitalisme Modal: dari
Wacana Menuju Gerakan (2002), dan Membela Agama Tuhan: Potret Gerakan Islam dalam
Pusaran Konflik Global (2003). Pengalaman lainnya yang menarik adalah pernah menjadi
produser untuk sebuah film dokumenter tentang Polisi DIY dan Masyarakat Transisi.
Aktivitas di rumahnya yang terletak di desa Lemwulung di wilayah Bangun Tapan, BantuI,
antara lain membaca novel roman, membaca puisi dan mengasuh anak bersama istri
tercinta.

Theorizing Risk & Uncertainty in Social Enquiry: Exploring the Contributions of Frank Knight

469C Bukit Timah Road Singapore 259772
Tel: (65) 6516 6134 Fax: (65) 6778 1020
Website: www.lkyspp.nus.edu.sg
Lee Kuan Yew School of Public Policy
Working Paper Series
Theorizing Risk & Uncertainty in Social Enquiry:
Exploring the Contributions of Frank Knight
Darryl S.L. Jarvis
Associate Professor
Lee Kuan Yew School of Public Policy
National University of Singapore
Email: Darryl.Jarvis@nus.edu.sg
May, 2009
Working Paper No.: SPP09-14
Keywords: Frank Knight, Risk, Uncertainty, Probability, Risk Measurement
2
Abstract
The problem of risk and uncertainty continues to plague social scientific enquiry,
ostensibly imposing epistemological limits to knowledge. This paper explores this issue
in relation to the writings and theoretical contributions of Frank Knight, one of the most
illustrious economic thinkers of the twentieth century. Knight’s contributions essentially
constructed a means for assessing and measuring risk in various facets of social activity,
seeding insights which remain pertinent to this day. As the paper notes, however, despite
Knight’s insights and the tri-partite methodological schema he constructed for probability
analysis, remarkably few social sciences have mined his work. Ironically, much that we
need to know to more effectively theorize and accommodate the conundrums of risk and
uncertainty into social scientific methods Knight long ago bequeathed us.
3
Introduction
When Donald Rumsfeld ruminated over the difficulties of prosecuting the War on Terror,
he confessed to reporters gathered at a briefing in the Department of Defense, that;
. . . the truth is, there are things we know, and we know we know them ─ the
known knowns. There are things we know that we don't know ─ the known
unknowns. And there are unknown unknowns; the things we do not yet know that
we do not know.1
Rumsfeld’s statement spoke to an enduring problem endemic to the conduct of foreign
affairs; decision making under conditions of uncertainty. In theaters of war it constitutes
the greatest of all enemies, the “fog of war,” where the unknown and or uncertain
attributes of enemies makes force deployment and the commitment of precious finite
military resources a high risk calculus. Military strategists throughout the millennia have
faced this dilemma; second guessing the motives, calculations, reasoning, capabilities and
likely actions of opponents, and through agile mental arithmetic, imagining futures and
scenarios where possible actions, events or interventions might outmaneuver rivals. Such
games are played out in the minds eye and involve a complex assessment of known facts,
discounting those facts thought less reliable, calculating how the contours of distant
realities might be imagined and assessed by opponents, while anticipating the response of
rivals to these imagined futures as a means of gauging what one’s own course of action
should be. To the victor goes the spoils of war, to the vanquished the weight if history.
That history should be decided by such calculations and their asymmetric nature, explains
both its capricious ebbs and flows and our inability to predict accurately its future. While
many might interpret this an outcome of luck, fortune, or the perils of reckless behavior,
it more obviously represents a habit ubiquitous to humankind: the process of peering into
the future, anticipating events yet to transpire and, on the basis of these imagined futures,
1 Donald Rumsfeld, Secretary of Defense, Department of Defense news briefing, The Pentagon, October
17, 2001 < transcriptid="3793">
4
making decisions and deciding on courses of action to intercept realities before they
emerge. Such calculations are the gist of all social actors; they make history and explain
its dissonant and myriad forms.
For Frank Knight, one of the most illustrious economic thinkers of the twentieth century,
such a predilection spoke not only to the fallibility of human beings but, more
importantly, to the limits of knowledge. For Knight, humankind was capture to a selfevident
peculiarity; the inclination to think, act and position ourselves in relation to
events and processes referenced in the future while tending to discount or ignore those of
the past and present. Strangely, Knight observed, despite the great advances in rationalist
epistemologies and the mass reservoirs of scientific knowledge produced, human thought
and action is defined as much by a consciousness of the future as by a prescience of the
past. Much of our thinking, Knight insisted; was a function of anticipatory futures; a
complex chess game of possibilities yet to emerge informing courses of action and
decisions before they have happened. As conscious beings we strive perennially for
knowledge of the future.2
Knight’s insights spoke to a recurrent problem in scientific enquiry of how social agents
think and act and on what information and under what conditions they premise their
calculations. How rational can “rational man” be if, in fact, he bases his deliberations on
facts that cannot be known or on realities which do not currently exist? Indeed, absent a
rational calculus of action to what extent can scientific precepts be applied to social
actors at all? Random subjectivity, incessant serendipity or the thought traits of countless
actors forever engaged in futurist navel gazing, might well be the constitutive elements of
the collective body-economic-politic. Much like a mirrored Panoptigon, human thought
and action could well prove to be as cursory, imprecise and emotive as the most visceral
belief systems used to validate truth.
2 Frank H. Knight (2002), Risk, Uncertainty and Profit. Washington D.C.: Beard Books, pp.199, 201.
5
It is, of course, easier to assume away such confusing flotsam and jetsam and embrace,
instead, neat and rationally precise epistemologies able to impose order, clarity and
explanatory consistency. As Knight well recognized, however, doing so would be to
render unlikely meaningful explorations into the social world and make improbable
progress in social enquiry.3 Fairly obviously, the messy prognostications of actors are
what drives history; they populate the market place, the corporate boardroom, the mind of
the political strategist and infuse our daily individual thinking. In the corporate
boardroom, for example, decisions to invest large sums of money are necessarily always
made in anticipation of future demand for products or services still on the drawing board.
Corporate strategy, by definition, is formulated in anticipation of the actions of
competitor companies which may or may not materialize. Individually, we decide to
enroll in a course of study on the basis of future perceived career opportunities, or decide
against courting a prospective partner having anticipated that the relationship will fail in
the future. Markets themselves are driven by such calculations. The equity market, for
example, is the raw expression of this carnal disposition to base current decisions on
perceived futures. When we invest into the stock market we draw little on the abundance
of factual information about the historical performance of equities; time-series valuations,
dividend yields, beta volatility measures, and price to earnings ratios data, so much as
base our decisions to buy, hold or sell almost exclusively on an imagined future and the
anticipation of unrealized events impacting the value of the equities concerned.4
Peering beyond the here and now and understanding the complex interstitial relations of
events yet to transpire and how human agents plan to interface with these and thus
change the circumstances that obtain, was, for Knight, the primary task of the social
3 The utility of rationalist epistemologies for understanding the social world Knight forcefully
problematizes by both embracing them and questioning the limits of their ability to deliver greater insight
and knowledge: “It has become somewhat the fashion, especially since Bergson came into vogue, to be
irrationalistic, and question the validity of logical processes. It seems to the writer that there is much
ground for this position. There is to my mind no question of understanding the world by any other method.
There is, however, much question as to how far the world is intelligible at all.” ibid., p.209
4 The “Beta value” refers to a quantitative measure of the volatility of a given stock relative to the
performance of the stock market as a whole.
6
scientist. Within this triangulated time-space dimension, he argued, knowledge ultimately
resided.
Knowing the Future: Risk, Uncertainty and Profit
Knight was not the first to recognize the conundrums of this perennial search for a
knowledge of the future. Social and political theorists like Weber, Durkheim, Marx and
Hegel had each postulated the vast expanse of social agency which made problematic
definitive conceptions of pure knowledge or a teleology of history’s trajectory. But while
many had pondered these dilemmas as abstract philosophical problems, Knight was the
first to make this ontological assumption his starting point, nesting in economic theory a
seed which spoke to its absolute limitations as a science of rational calculation. For
Knight, the disposition of human consciousness to “perceive the world before we act to it,
and react not to what we perceive, but always to what we infer,” defined the limits of the
rational universe.5 We are and remain, Knight insisted, creatures of anticipation.
For Knight, the problem of course rested in constructing this knowledge of the future and
understanding both the attributes that shape its constitution but also the limits of its
accuracy. Knight famously captured the essence of this dilemma in his opus dictum,
Risk, Uncertainty and Profit (RUP), widely celebrated for its explorations into the
elemental problem of a future knowledge constrained by the vicissitudes of inference,
perception and anticipation. It was these, Knight argued, that defined the space in which
human kind is forced to think and act: thinking and action under conditions of
uncertainty. For Knight, this was the great conundrum. Indeed, it posed for the social
scientist specific problems associated with the acquisition of theory-knowledge and, more
importantly, spoke to the absolute limits of this knowledge as a means of predicting and
understanding human behavior.
Uncertainty and the Limits of Knowing
5 Frank H. Knight (2002), op.cit, p.201.
7
Knight’s work is the seminal statement of the analytical distinction between risk and
uncertainty. “If we are to understand the workings of the economic system,” he wrote, we
must first “examine the meaning and significance of uncertainty.”6 But why? Why the
need to differentiate the concept of uncertainty from that of risk? Knight’s reasoning is
intricate and sophisticated, and ultimately predicated upon his philosophical questioning
of the limits of rationalist epistemologies. As an economist and student of philosophy in
the early part of the twentieth century, Knight was confronted by a near universal
embrace of rationalist thinking; increasingly intricate and abstract forms of empiricaltheory
knowledge that presumed scientific discovery of every facet of economy and
society was possible. Explanation and the discovery of insight was increasingly viewed
merely as a matter of the application of rigorous, scientific precepts, of the collection of
greater numbers of facts, the discovery of greater numbers of causal associations, and
thus the revelation of historical patterns which could explain human action in the
economy and in social matters generally. In the early part of the twentieth century, the
zest for science and the great leaps forward it had enabled in virtually all facets of human
endeavor, naturally disposed its celebrants to assume that these same precepts could be
applied in the social affairs of a society; to the workings of the economy and the
organization of its politics. Science and rationalist epistemologies disposed its adherents
to see a world only of increasing certainty. The secrets contained within the functioning
and operations of the economy, its tendencies to boom and bust, or the machinations of
full employment and financial stability, had simply to be unearthed through discovery.
The secrets of homo economucis would soon reveal themselves.7
6 Frank H. Knight (2002), op.cit, p.199.
7 The optimism embodied in the belief structure around the capabilities of science, quantification
techniques and the role of measurement and objective assessment, emboldened adherents in the Nineteenth
and early Twentieth century to assume no facet of society could not be both understood and any ills
rectified. Adherents like Francis Edgeworth, an economist, and Frank Ramsey, a Cambridge mathematician
writing in the 1920s, proposed respectively the development of a “hedonimeter” and a
“psychogalanometer,” presumably to measure those mechanisms best able to produce optimal outcomes for
both society and the individual. As quoted in Peter L. Bernstein (1998), Against the Gods: The Remarkable
Story of Risk. John Wiley & Sons, pp.191-192.
8
Knight’s departure from this rationalist optimism is seeded in his rejecting neoclassical
positivism and by insisting on the need for economics to reframe itself as an
interpretative social science, in essence for economics to understand the differences
between the natural and social universes. This required, Knight insisted, “some inquiry
into the nature and function of knowledge itself.”8
Natural Science Versus Social Science: The Limits of Rationality and the Poverty of
Economic Method
For Knight, “the fundamental difference in the case of animal or conscious life is that it
can react to a situation before that situation materializes; it can ‘see things coming’.”9
This gives rise to a series of elemental postulates the social scientist must be cognizant of.
First, that “the universal form of conscious behavior is . . . action designed to change a
future situation.” Second, that this form of consciousness involves perception and a
twofold form of inference: “we must infer what the future situation would have been
without our interference, and what change will be wrought in it by our action.”10 And
third, that perception and inference are not infallible processes. The precise dimensions of
perception versus inference Knight insists is immaterial. The point, rather, is that the
“function of consciousness is to infer, and all consciousness is largely inferential,
rational.”11 As Knight observes, the fact that we as social actors “do not perceive the
present as it is and in its totality, nor do we infer the future from the present with any
degree of dependability, nor yet do we accurately know the consequences of our own
action,” renders the categorical realm of uncertainty the primary condition under which
action and knowledge always obtain.12 The limits to knowledge, or at least the paths to
knowing, were, for Knight, thus circumscribed by these elemental philosophical
considerations.
8 Frank H. Knight (2002), op.cit, p.199.
9 ibid., p.200.
10 ibid., p.202
11 ibid., p.203.
12 ibid., pp.202-203
9
The base knowledge we have as social scientists thus rests on the observation that “things
not present to sense are operative in directing behavior, that reason, and all
consciousness, is forward-looking; and an essential element in the phenomena is a lack of
. . . accuracy . . . [and the tendency toward] . . . error.”13 This intricate schema defines for
Knight the problem of social based knowledge. Social based phenomena display unique
characteristics. “What is observation and what is inference are questions on par with what
is truth,” Knight suggested. They essentially remain perennial such that “we cannot
separate the discussion of reality from the discussion of the knowledge of reality, the
nature and structure of thinking and the conditions of its validity.”14
Under these conditions, neoclassical positivism proves entirely unsuitable for revealing
anything about the social-economic world:
Concrete and positive answers to questions in the field of economics science or
policy depend in the first place on judgments of value and procedure, based on a
broad, general education in the cultural sense, and on ‘insight’ into human nature
and social values, rather than on the findings of any possible positive science.
From this point of view, the need is for an interpretive study (verstehende
Wissenschaft) which, however, would need to go far beyond any possible
boundaries of economics and should include the humanities as well as the entire
field of social disciplines.15
For Knight, “Economics and other social sciences deal with knowledge and truth of a
different category from that of the natural sciences, truth which is related to sense
13 ibid., p,203.
14 Frank H Knight (1956), On the History and Method of Economics: Selected Essays. Chicago: University
of Chicago Press, p.159. See also the discussion in R.A Gonce (1992), Frank H. Knight on Social Control
and the Scope and Method of Economics,” in Mark Blaug (ed.), Pioneers in Economics 37: Frank Knight
(1885-1972), Henry Simons (1899-1946), Joseph Schumpter (1883-1950). Aldershot: Edward Elgar, pp.22-
33.
15 ibid., p.177. See also the discussion in Tony Fu-Lai Yu (2004), “Frank H Knight’s Thought Revisited:
Subjectivism, Interpretation and Social Economics,” International Journal of Social Economics, 31(7),
p.659. See also further discussion of the role of science in economic theory in Frank H. Knight (1935) “The
Limitations of Scientific Methods in Economics,” in Frank H. Knight (1935), The Ethics of Competition.
London: George Allen & Unwin, pp.105-147.
10
observation ─ and ultimately even to logic . . . “16 The precision required of the natural
sciences in the observation and correlation of facts and behaviors to events is beyond the
scope of economic theory, since human action and conduct are related to factors which
are not observable or testable.17
Rank positivism is thus rejected by Knight but not in favor of a fundamentally less
rigorous analytical framework, but a more interpretative one.18 Specifically, Knight
suggests the need to contextualize the types of knowledge produced by humans on the
basis of their ability to interface with events, foresee them, and thus change the outcomes
that obtain.19 Economics, Knight insists, might thus be better situated:
16 Frank H Knight (1956), op.cit, pp. 154-155.
17 Knight’s philosophical questioning of positivist economic method is addressed extensively in his volume
Freedom and Reform: Essays In Economics and Social Philosophy (1947). Tellingly, he writes; “All
discussion is really critical and philosophical, even in the realm of facts. The decisive problems and
discussions of science deal with method; for discussion of what is true runs largely in terms of the methods
of inquiry and proof. In the field of law they deal with moral philosophy. Science is instrumental
knowledge ─ knowledge of facts about properties and behavior of things (including persons) with reference
to using them as instruments for given ends. But ‘valid’ science is social knowledge. As the deeper
problems of science itself have to do with method, they are critical and philosophical; the noninstrumental
interest in truth is a philosophical and an ethical interest.” Frank H. Knight (1947), Freedom and Reform:
Essays In Economics and Social Philosophy. New York: Harper & Brothers, p.218. See in particular
chapter VIII, “Science, Philosophy, and Social Procedure.”
18 Knight’s interpretative stance should not be confused with a relativist one. Knight remained committed to
the precepts of scientific enquiry and to rationality as an otology suited to investigating the social world.
Rather, he favored a normative cum contextualist approach which he argued should be fiercely critical,
interrogating categories like “rational actor” as core assumptions on which knowledge systems could be
built and universally applied. The model of positivist, value neutral science so favored by most of his
contemporaries as the appropriate method for economists, Knight thought both ill-suited and flawed in its
ability to provide insight, purposeful action and guidance in understanding economic behavior and helping
moderate it toward good social outcomes. As James Buchanan notes of Knight, “[a]lthough he remains far
from accepting a purely relativistic position, Knight refrains from either asking or answering the question
concerning the existence of some ultimate and unchanging reality. ‘Truth’ is measured only by agreement
or consensus among informed persons, despite the acknowledged questions that this definition begs.” See
the discussion in James M. Buchanan (1967), “Politics and Science: Reflections on Knight’s Critique of
Polany,” Ethics, 77(4), July, p.304. In contrast, William Kern prefers to characterize Knight as a skeptical
scientist, not anti science but intensely inquisitive, a trait which probably accounts for the seeming paradox
of Knight both embracing and rejecting the methods of science and rationality, or at least wanting to keep
them in more constrained boxes and ascribe less explanatory and analytical weight to them than was
popularly accepted. As Kern notes, Knight’s inquisitive reflection on the method of economics earned him
the reputation as “the external asker of questions.” William S. Kern (1990), “Frank Knights Skeptical View
of Economic Education,” The Journal of Economic Education, 21(2), Spring, p.196. Knight’s thinking on
issues of science, economic method and philosophy is further revealed through his extensive private
correspondence. See Warren J. Samuels (1977), “The Knight – Ayres Correspondence: The Grounds of
Knowledge and Social Action,” Journal of Economic Issues, XI(3), September, pp.485-525.
19 Frank H. Knight (1956), op cit. pp.158-165.
11
. . . in the field of art, and not of science, of suggestion and interpretation, and not
accurate, definite, objective statement, a sphere in which common sense works
and logic falls down, and where, in consequence, the way to improve our
techniques is not to attempt to analyze things into their elements, reduce them to
measure and determine functional relations, but to educate and train our intuitive
powers.20
For Knight the result is an imperfect or critical knowledge but one wholly superior to the
methodological flaws of positivism and scientific rationalism that assume certainty and
predictability; that the building blocks of social activity could be dissembled into their
constituent parts, causal associations established and thus predictive models
constructed.21 In contradistinction, the social and economic world Knight saw as much
messier and much harder to understand and analyze. Much of what we do as social actors
is far from “rational” in the narrow economic sense. In fact, he noted, “it probably
occasions surprise to most persons the first time they consider seriously what a small
portion of our conduct makes any pretense to a foundation in accurate and exhaustive
knowledge of the things we are dealing with.”22 How we calculate decisions and decide
on courses of action arises as much from ill informed supposition and serendipitous
encounters with partial information, irrational conduct and idiosyncratic impulses, as it
does serious rational calculation of events and phenomena; “[t]he ordinary decisions of
20 Frank H. Knight (1924), “The Limitation of Scientific Method in Economics,” in R. Tugwell (ed.), The
Trend in Economics. New York: Alfred A. Knopf, p.247.
21 Knight’s dismissal of positivism and empiricism as an epistemological basis for understanding
economics and economic behavior is most forcefully expressed when he writes; “It is not conceivably
possible to ‘verify’ any proposition about ‘economic’ behavior by any ‘empirical’ procedure, if the key
words of this statement are defined as they must be defined to be used with relevance and precision.” This
debate, of course, reified the distinction between what Knight identified as “theoretical economics” and
mathematical or “empirical economics,” and set off fierce condemnation from many of his peers. See Frank
H. Knight (2002), op.cit., pp.3-21; T.W. Hutchison (1941), “The Significance and Basic Postulates of
Economic Theory: A Reply to Professor Knight,” The Journal of Political Economy, 49(5), October,
pp.732-750. See also Morris A. Copeland (1925), “Professor Knight on Psychology,” The Quarterly
Journal of Economics, 40(1), November, pp.134-151; James M. Buchanan, (1967), “Politics and Science:
Reflections on Knight’s Critique of Polany,” Ethics, 77(4), July, pp.303-310.
22 Frank H. Knight (2002), op.cit., p.210.
12
life are made on the basis of estimates of a crude and superficial character.”23 Life is
more than economics and rational conduct, it is “rivalrous and contentious . . . and less
than perfectly rational” and fraught with an eye to the future that bases decisions and
calculations on non-known phenomena and events whose reality is only imagined to exist
at some future juncture.24 It is as if, he wrote, “Men ‘exist’ in several different universes
of reality, between which philosophy has built no adequate bridges, and does not seem to
be in the way of doing so.” Each of these universes contain their own truth, and each may
contradict the truth of other universes.25 Understanding the contingent interrelationships
between these universes and how they connect was, in a sense, Knight’s life work.
Risk, Probability and Uncertainty: Three Knightian Typologies
Knight’s ingenuity and gift to economic theory was his ability to understand the
agility of concepts readily employed but little theorized. Three key concepts seem to
suffer such a fate in economics and, more importantly, seem to capture assumptions
about the causal relationship between subjective social processes and objective
phenomena; risk, uncertainty and probability ─ or chance. Each portend to a
different knowledge-set but, in economics, had been treated ubiquitously as if all
three categories could be captured through measurement and subject to rational
processes of calculation in respect of the frequency of their recurrence, the
underlying causality responsible for their generation, and the magnitude of their
impact on the phenomena being observed. Knight rejected this outright and forever
changed discourse on risk in economics and the assumptions on which it was based.
Risk, probability and uncertainty, he insisted, were entirely different creatures:
23 ibid., p.210. See also J. Patrick Raines & Clarence R. Jung, Jr (1992), “Schumpeter and Knight on
Economic and political Rationality: A Comparative Restatement,” Journal of Socio-Economics, 21(2),
pp.109-124.
24 J. Patrick Raines & Clarence R. Jung (1992), op.cit., p.121.
25 John McKinney (1977), Frank H. Knight on Uncertainty and Rational Action,” Southern Economic
Journal, 43(4), April, p.1439.
13
Uncertainty must be taken in a sense radically distinct from the familiar notion of
Risk, from which it has never been properly separated. . . . It will appear that a
measurable uncertainty, or “risk” proper . . . is so far different from an
unmeasurable one that it is not in effect an uncertainty at all.26
Knight’s principal contribution thus rested in his disentangling the concepts of risk and
probability from that of uncertainty. A great deal of RUP is therefore devoted to the
analytical articulation of these concepts. While uncertainty, for Knight, represents the
Achilles heel of social enquiry, both the problem to investigate and the obstacle to greater
knowledge and understanding, risk, chance or probability, in contrast, lend themselves to
statistical based analyses and thus application in all facets of actuarial and probability
based research; the modern bedrock of the insurance and finance industries. As Knight
well demonstrated, however, the precision of these knowledge instruments had also to be
problematized.
1. Knightian Risk
Risk, for Knight, arises from the objective observation of events and phenomena, from
observable causalities whose frequency, severity, and magnitude of impact or
consequences can be reasonably assessed. Risk, in other words, is a measurable entity
whose magnitude can be inferred through formal inductive logic. For Knight, risk is
therefore tangible and quantified through the calibration of observable facts with the
frequency of their recurrence. Knight invokes the famous example given by the French
classical economist, Von Mangoldt, and the bursting of Champagne bottles, to arguer his
point.
In the Eighteenth century the production of Champagne was not for the faint of heart.
Wide variance in the manufactured quality of glass champagne bottles made for
explosive situations. As still wine is mixed with sugar and yeast or liqueur de triage, the
26 Frank Knight as quoted in Peter L. Bernstein (1998), op.cit., p.219.
14
fermentation process releases large amounts of carbon dioxide into the wine and pressure
in the bottle slowly builds to 80-90 pounds per square inch; about three or four times the
pressure in a car tyre. Not surprisingly, in the early years of Champagne production
imperfect bottles tended to explode with great frequency, in some instances producing
now legion stories of large chain reactions through the inappropriate storage of bottles
which led to huge financial losses for the champagne producers concerned.
As Mangoldt and Knight observed, however, the fact that bottles of champagne exploded
frequently did not itself introduce an uncertainty or hazard into the production of
Champagne “since in the operations of any producer a practically constant and known
proportion of the bottles burst.” The rate of failure of champagne bottles is thus known
and “the loss becomes a fixed cost in the industry and is passed on to the consumer, like
the outlays for labor materials or any other.”27
The point for both Mangoldt and Knight was that bottles of champagne exploding was a
contingent risk, the magnitude of which could be calculated through recurrent
observation, the costs associated with a certain percentage of the bottles failing factored
into the price of the bottles that survived, and thus the costs of production underwritten.
The risk, as such, could be managed and the consequences to the business mitigated.
Contingent risk, in this instance, is a risk that is not certain or even necessarily probable,
but as Knight famously observed “if the numerical probability of its occurrence is known,
conduct in relation to the situation in question may be ordered intelligently.” In other
words, contingent risks could be compensated for and or mitigated in terms of their
impact on the actors concerned.
These definitional principles of risk we now recognize as the bedrock of the insurance
industry. No one, for example, can know if a particular building will catch fire and burn
to the ground and few businesses operate on a scale that would allow them to calculate
the fixed cost that fire represents to their business. The insurance industry, however, does
27 Frank H. Knight (2002), op.cit., p.213.
15
operate on such a scale. By aggregating industry wide contingencies of building fires it is
able to calculate the fixed cost fire represents to a particular industry. On the basis of
these calculations, insurance products can be offered to business operators, converting the
contingency cost of fire into the fixed cost of the insurance product that is then passed on
to the consumer. As Knight notes, it makes no difference “whether the grouping of cases
is effected through a mutual organization [a business association, for example] of the
persons directly affected or through an outside commercial agency.”28 The principle
remains the same.
While, however, the principle remains the same it has been the advent of a commercial
insurance industry that has enabled the conversion of contingent risks into fixed costs and
catapulted the ability of industry, commerce and individuals to mange so many of the
contingent risks they face in everyday life. Insurance aggregates the contingent risks of
large numbers of people or organizations and, in doing so, enables individuals and
organizations to enjoy the advantages provided by the Law of Large Numbers. The Law
of Large Numbers essentially expresses the idea that in a random process as the number
of trials increases the percentage difference between the actual and expected outcomes
approaches zero. The idea is normally highlighted with reference to Jacob Bernoulli’s
theory for calculating probabilities.29 Tossing a coin, for example, will result in either the
coin being heads or tails. Probability theory tells us that the chances of the coin being
heads or tails is 50/50 for each toss. Several tosses of the coin in a row, however, may
result in an unequal number of heads or tails in succession. The Law of Large Numbers
does not tell us that with repeated throws the ratio of heads to tails will approach a 50/50
mean, but rather that the variance between the observed and true average will diminish.
As Peter Bernstein expresses it, what the Law of Large Numbers “tells us is that the
average of a large number of throws [of a coin] will be more likely than the average of a
28 ibid., pp.213, 247.
29 The Law of Large Numbers is sometimes referred to as Bernoulli law of large numbers. See Peter L.
Bernstein (1998), op.cit, pp.121-123.
16
small number of throws to differ from the true average by less than some stated
amount.”30
While a seemingly innocuous observation, for the insurance industry and, indeed, for
those of us who use commercial insurance products, the Law of Large Numbers
represents one of the great revolutions in the management and mitigation of contingent
risk. It not only provides a greater variety of monetized instruments to compensate and
manage for risk exposure but at a cost to the end user that diminishes relative to the size
of the contingent risks each of us face. In essence, the Law of Large Numbers enables the
insurance industry to manage the risk exposures it faces with greater certainty. It allows
insurers to increase the accuracy of the expected deviation from probabilities concerning
their risk exposures within a given business segment or population and, in a competitive
insurance environment, allows for more accurate risk pricing of insurance products. The
Law of Large Numbers thus represents a kind of economies of scale for the insurance
industry that, because of the spread of the cost of contingent risks among numerous
actors, reduces the relative cost to the end user of mitigating contingent risks. In the
modern era, the management of contingent risks has thus never been cheaper for the
individual actor or as profitable for the insurer.
While, however, the Law of Large Numbers makes the insurance industry possible, it
also defines the outer limits beyond which insurance cannot operate. It requires, for
example, that “the risks insured must be both large in number and independent of one
another,” but, more importantly, that the phenomena to be insured must be amendable to
rational calculation. 31 By definition, as Knight recognized, only contingent risks are
amenable to rational calculation:
If in a certain class of cases a given outcome is not certain, nor even extremely
probably, but only contingent, but if the numerical probability of its occurrence is
30 ibid., pp.121-123;204-205. See also Kenneth J. Arrow (1971), Essays in the Theory of Risk Bearing.
Chicago: Markham Publishing Company.
31 ibid., p.204.
17
known, conduct in relation to the situation in question may be ordered
intelligently.32
In practical terms this limits the provision of insurance to areas where the prospects for
loss can be calculated or at least the contingent costs of insurance exposures can be
reasonably estimated. It also limits the provision of insurance to phenomena that are nonrelational.
Insurance, for example, can be offered against theft but never where theft is
related to the activities of the policy holder. Likewise, insurance can be offered against
the risk of fire, flood, accident, loss of life, disability, or instances of malfeasance or
misfeasance, but not against the risk that a book will fail to become a bestseller, that the
colour red will be the winter season’s new fashion trend, or that a particular variety of
cookie will acquire a dominant market share.33
As Knight and others recognized, however, just because risks are contingent does not
always render their calculation transparent or easy. Contingent risks are more often
opaque than transparent and measuring their occurrence and frequency fraught with
numerous technical problems. Disentangling instances of arson from acts of negligence,
oversight, mechanical malfunction, lightening, or mishap and accident in the case of fire,
for example, involves all manner of calculations, assumptions, interpretation and
inference. Similarly, calculating the contingent risks to shipping presents numerous
challenges in terms of the contingencies of weather at various points along a ship’s route,
the time of year passage occurs, the unpredictability of sudden weather related events
(fog and collision risk, for example), the possibility of rogue waves and damage to the
vessel and cargo, and the relationship of these factors to the build of the vessel, the
32 Frank H. Knight (2002), op.cit., pp. 212-213.
33 Malfeasance or misfeasance operates in relation to the privity of contract. A contract creates a future
obligation. Where a signatory to the contract fails to deliver on that obligation, this is referred to as
nonfeasance (non-delivery on contractual obligations). Where a signatory to the contract performs the
obligation negligently, only partially or in a sub-standard manner, this is said to be misfeasance. And where
a signatory sabotages his/her contractual obligations and attempts to inflict intentional damage, this is said
to be a case of Malfeasance. See Robert Merkin (2000), Privity of Contract: The Impact of Contract (Rights
of Third Parties) Act 1999. London: LLP; Geoffrey Cheshire (2001), Cheshire, Fifoot and Furmston’s Law
of Contract. London: Butterworths (14th Edition).
18
manner of its operation, the possibility of navigational error and the expertise and
experience of its crew.34
As Knight observes, even in instances of contingent risks, much of what we do relies
conspicuously on the measurement of “uncertainty through the classification of
instances.”35 In the case of the shipping industry, for example, the Lloyds Register of
Shipping is essentially little more than a medium for classifying the build attributes of a
ship; its hull capabilities and its onboard equipment with reference to its design
specifications and confirmed through periodic independent inspections. As a maritime
insurer, Lloyds of London uses this information as a means of assessing and classifying
the seaworthiness of a vessel and its likely ability to handle all manner of weather and
ocean related events. The contingent risks a ship may encounter, in other words, Lloyds
of London or any other insurer would find too difficult if not impossible to assess and or
calculate the fixed cost to the vessel operators. Instead, they rely on “the classification of
instances” rule as a means of correlating the build attributes of a ship, the equipment it
has on-board, and the training standards of the crew as a means of calculating the
probability of the vessel to navigate safe passage through the world’s oceans. The
classification of instances thus allows Lloyds of London to correlate the frequency of loss
of a certain class of vessel and to price insurance based on a ship’s attributes rather than
having to identify specific contingent risks the vessel might encounter over its ocean
going life. Insurance companies, Knight observed, had stumbled across a practical and
parsimonious method for converting the uncertainty of various risk events (weather rogue
waves, navigational error, etc) and the uncertainty of their distribution through time, into
a simple yet highly effective tool for calculating the probability of their occurrence and
thus allowing all manner of risks to be managed. Indeed, it was the subjugation of risk
events to probability analysis that, for Knight, offered the most effective way forward and
defined implicitly an avenue down which all risk analysis must travel.
34 Frank H. Knight (2002), op.cit., pp.249-250.
35 ibid., pp.246-247.
19
2. Knightian Probability and the Classification of Instances
Like the insurance industry, Knight too understood the problem of ascribing specific
values to contingent risks and that for many such risks direct sense observation was
impractical as a means of determining their frequency and severity. Mangoldt’s bursting
champagne bottles, for example, easily allowed the identification of what Knight termed
“an association between predicates” where risk in Champaign production reflected
simply a relationship between the quality of the glass bottles produced, the fermentation
process and the breakage rate. As Knight observes, however, “it will be evident that the
practical difficulties of ordering conduct intelligently are enormously increased where . . .
[risk causality] . . . is contingent rather than being positive.”36 What happens when the
causal factors responsible for generating risk are only occasional, or where the
“demonstration of a dependable connection is vastly more difficult,” or where, for
example, there is the additional problem of “ascertaining the precise proportion of cases
in which the connection occurs?”37 Lung cancer, for example, can be correlated to
smoking but smoking does not ensure the development of lung cancer and the presence of
lung cancer can occur in the absence of a history of smoking. The “association between
predicates” in the case of lung cancer is thus vastly complicated by a plethora of factors;
the number of years an individual has been smoking — if at all, the number of cigarettes
smoked per day, possible exposure to passive smoking, the relative health and age of the
individual, their genetic composition as well as a variety of other lifestyle habits. It is,
obviously difficult if not impossible to observe all these correlates or the chain of
causality represented by them.
Knight’s solution to the apportionment of values in ascertaining risk is to turn to
probability as a means for calculating the possibility of outcomes. “We have to estimate
the given factors in a situation and also estimate the probability that any particular
consequence will follow from any of them if [original emphasis] present in the degree
36 ibid., pp.213-214.
37 ibid., p. 214
20
assumed.”38 To do so, Knight develops a classification “scheme for separating three
different types of probability situation”:39
i. a priori probability
ii. Statistical probability
iii. Estimated probability
a priori probability
a priori probabilities are derived deductively. The roll of a dice, for example, has a one in
six chance of a particular number being rolled. Games of chance where defined
parameters exist for certain outcomes to ensue are what Knight meant when he referred to
a prior probabilities. The outcomes, in other words, have a defined universe, but the
order in which the will occur, i.e., whether the roll of a dice will result in a 1, or 3 or 6,
for example, can only be estimated in relation to its (one in six) probability of occurrence
for each throw of the dice.
What is unique about a priory probability for Knight, then, is the fact it speaks to a
defined classification of instances. The risk outcomes are known and there is no
possibility of deviation from these save for nefarious activity in terms of rigging the dice.
A priori probability thus represents an “absolutely homogeneous classification of
instances” and, for Knight, “is on the same logical plane as the proposition of
mathematics” in as much as “the chances can be computed” through the application of
general principles.40
While, however, a useful heuristic tool a priori probability has limited application as a
means for understanding the general universe of risks we face and the probability of their
distribution. As Knight notes, “a mathematician can easily calculate the probability that
38 ibid.
39 ibid., pp. 224-225.
40 ibid., pp. 224-225.
21
any proposed distribution of results will come out of any given number of throws” of a
dice, but no finite number of throws of a dice can give certainty to the actual distribution
of results that will arise. Uncertainty in terms of the distribution of risks thus still obtains
in the case of a prior probability. It would, Knight argues, “be ridiculous to suggest
calculating from a priori principles the proportion of buildings to be accidentally
destroyed by fire in a given region and time” let alone to know which buildings and at
which times they will burn.41 In this instance, the risk outcomes are known, fire, but the
ability to calculate with certainty the distribution of these risks and when they will occur
is beyond the ability of the mathematician and the principles of a prior probability.
Statistical Probability
As mathematically precise as a priori probability might be its penultimate limiting factor
is that this type of probability is practically never met with in business or another facet of
social, political and economic activity. It is, Knight observed, difficult to think of a
business hazard in which it is “possible to calculate in advance the proportion of
distribution among the different possible outcomes.” In the absence of a defined universe
of outcomes the types of risk business face must be dealt with by “tabulating the results
of experience.” 42 Knight refers to this form of “tabulation” as statistical probability
which he defines as the “empirical evaluation of the frequency of association between
predicates” but which are “not analyzable into varying combinations of equally probable
alternatives.”43
“The main distinguishing characteristic of this type [of probability] is that it rests on an
empirical classification of instances.”44 Where the universe of outcomes is not defined as
with the throw of a dice, Knight’s suggestion for dealing with the exigencies of social and
economic risk was parsimonious: define categories of risk through classifying
41 ibid., p.215.
42 ibid.
43 ibid., p.225.
44 ibid. See also Stephen F. LeRoy and Larry D. Singell (1987), “Knight on Risk and Uncertainty,” The
Journal of Political Economy. 95(2), April, pp. 397.
22
experiential instances and then tabulate the frequency of like instances as a means of
calculating their probability for recurrence. In doing so, much if not most of the risks
business face Knight insisted could be reduced to a fair degree of certainty. The process
of statistical grouping, in other words, while it would not necessarily reveal patterns
would generate frequencies and thus a means of calculating their nominal probability.
For Knight, statistical probability provided the one concrete tool for dealing effectively
with common forms of risk endemic in social and economic activity; indeed for helping
business and economic actors to provision and plan for risk contingencies. But, as
Knight acknowledged, statistical probability is limited by its inability to gain the same
“degree of homogeneity in the instances classed together” as in, for example, a priori
probability. The throws of a dice represent a situation of absolute homogeneity where
each throw is perfectly comparably and identical to any other throw of the dice. By
contrast, in the case of statistical probability the classification of instances relies on a
statistical grouping of phenomena that are not strictly homogenous. Classifying buildings
in order to understand the probability of fire, for example, relies on a statistical grouping
of buildings and building types that by definition are dissimilar. The materials used in
construction, the build quality of the structure, its age, location, the fire retarding
attributes of the internal walls and countless other factors make classification
problematic. The problem of defining groups as accurately as possible thus reduces the
nominal accuracy of statistical probability. The problem is succinctly expressed by
Knight:
The practical differences between a priori and statistical probability seems to
depend upon the accuracy of classification of the instances group together. In the
case of the die, the successive throws are held to be alike in a degree and a sense
which cannot be predicated of the different buildings exposed to fire hazard.
There is, of course, a constant effort on the part of the actuary to make his
classifications more exact, dividing groups into subgroups to secure the greatest
23
possible homogeneity. Yet we can hardly conceive this process being carried so
far as to make applicable the idea of real probability in a particular instance.45
Classifying instances is thus fraught with an obvious tension: the need to form statistical
groupings in order to generate meaningful probabilities about the frequency of recurrence
while logically confronted by the reality that no two things are exactly alike.46 As Knight
suggested, however, this is a relative and graduated problem that represents a kind of
sliding scale from highly homogeneous to highly heterogeneous. Some phenomenon are
inherently more homogeneous, some less so. The flooding of domestic households, for
example, makes for a relatively homogeneous classification of instances in terms of the
predicates that cause flooding (geographic location, water hydrology and weather related
events) and the resultant outcomes. Domestic household burglaries, by contrast, make for
a less homogeneous classification of instances since the security measures vary widely
between households and the contents peculiar to them in terms of artwork, jewelry,
personal effects and the value attached to each are dissimilar. Both are amendable to
statistical probability analysis, but the degree of homogeneity is a graduated.47
Actuaries, of course, strive constantly to compensate for statistical errors by modifying
for instances of heterogeneity by imputing coefficients to allow for differences that are
frequently or always present in classes of instances. But again this issue for statistical
probability is such as to limit its absolute accuracy.
45 Frank H. Knight (2002), op.cit., p.217. See also James L. Athearn (1971), “What is Risk,” The Journal of
Risk and Insurance, 38(4), December, pp.639-645.
46 ibid., p.227.
47 Jochen Runde (1998), “Clarifying Frank Knight’s Discussion of the Meaning of Risk and Uncertainty,”
Cambridge Journal of Economics, 22, p.541. The problem of statistical grouping and thus of statistical
probability is a relative one, however, as Knight recognized. There is, he noted, a “graduation of probability
situations” by which he meant some things could be more accurately grouped and classified together
compared to others. Grouping orange producers together for the purpose of understating crop failure due to
weather related risks, for example, is inherently less graduated and relatively homogenous despite variation
in terms of different varieties of oranges, the role of geographic location, or variation in crop maintenance
habits by farmers or the systems they practice to avert frost, heat, or sunburn damage to the crop. The
existence of graduated probability situations thus allows for an inverse relationship between the accuracy of
statistical probability in situations of low graduation compared to higher error ratios in highly graduated
statistical probability situations (i.e., as heterogeneity in statistical grouping becomes greater). See the
discussion in Jochen Runde (1998), op.cit., p.542.
24
Estimated Probability
Both a priori and statistical probability make possible the management of risk in terms of
defining the possible universe of outcomes and nominally calculating their probability to
recur. But what if the universe of outcomes cannot be defined? What if the circumstances
that obtain are so unique or the outcomes so infrequent that it is meaningless to tabulate
experience as a measure of their probability? Or, what if there is no way to classify
instances because they are strictly non-comparable or represent such a complex degree of
interrelated contingencies that attempts to isolate the variables responsible for causality
are meaningless? Knight refers to this type of probability situation as estimates. For
Knight, the distinction is that “there is no valid basis of any kind for classifying
instances.”48 Instead, all that we have at our disposal to understand this universe of
uncertainty is to generate estimates. Knight’s third probability situation thus returns to
the problem of uncertainty as distinct from risk, where in the absence of a universe of
probable (statistical probability) or known outcomes (a priori probability), we are forced
to make a “judgment of probability” (estimated probability) and infer a universe of
possible or likely outcomes.
For Knight, of course, situations of estimated probability comprise the vast bulk of our
social and economic universe. Much of what we confront and do in life falls to this form
of calculation. Knight uses a common business example to highlight his point. Consider,
he says, a manufacturer contemplating expanding their business. To do so they will have
to expend more resources, perhaps acquire additional debt, increase the size of the
production facilities and hire more people. These costs can be readily calculated. But
what of the calculations about the viability of this course of action? Will added capacity
depress returns or initiate a price war with competitors? Will the income stream
generated by the additional capacity be enough to service the increased costs of the
additional hires, the factory expansion and the higher debt service? What if interest rates
48 Frank H. Knight (2002), op.cit., p.225
25
go up, or new market entrants with lower operating costs enter the market? Will the
economy and demand for the product remain strong? Will competitors move production
offshore and undercut retail pricing?49
As Knight asks, “what is the ‘probability’ of error . . . in the judgment?” of the
manufacturer deciding to expand production?50 Obviously, notes Knight:
. . . it is manifestly meaningless to speak of either calculating such a probability a
priori or of determining it empirically by studying a large number of instances.
The essentially and outstanding act is that the ‘instance’ in question is so entirely
unique that there are no others or not a sufficient number to make it possible to
tabulate enough like it to form a basis for any inference of value about any real
probability in the case we are interested in. The same obviously applies to . . .
most conduct and not business alone.51
The point, for Knight, is that the manufacturer does in fact perform a type of probability
analysis about the viability of their chosen course of action but also forms an estimate of
the likely probability that their estimate is correct.52 Knight was not discounting the value
of estimates but attempting to highlight that even in circumstances where the universe of
outcomes cannot be known, attempts to delineate a universe of potential outcomes
through rational calculation is possible albeit subject to higher error probabilities.
For many, however, estimates undoubtedly appear an inferior or at least a problematic
means for calculating potential outcomes because of their subjective nature and
indeterminate means of measurement. Knight both accepts and rejects this interpretation.
49 See also C. Robert Taylor (2003), “The role of risk versus the role of uncertainty in economic systems,”
Agricultural Systems, 75, pp. 251-264; Jochen Runde (1998), Clarifying Frank Knight’s Discussion of the
Meaning of Risk and Uncertainty,” Cambridge Journal of Economics, 22, pp.539-546; David Dequech
(2003), “Uncertainty and Economic Sociology: A Preliminary Discussion,” American Journal of
Economics and Sociology, 62(3), July, pp.509-532.
50 Frank H. Knight (2002), op.cit., p.226.
51 ibid.
52 ibid. See also the discussion in Tony Lawson (1988), “Probability and Uncertainty in Economic
Analysis,” Journal of Post Keynesian Economics, 11(1), Fall, pp.38-65.
26
Individuals by their very circumstances, he insists, must deal with an uncertain universe.
Attempting to eliminate uncertainty, or at best reduce it to risk by developing estimates,
Knight sees as a perfectly valid and rational human response. 53 Individuals do so by
invoking as objective an assessment as possible of the known facts, imagining future
ones, weighting these and then applying rational precepts to anticipate possible
outcomes.54 There is nothing irrational about this, Knight insists, but simply the
subjective application of objective rationality to situations of uncertainty. Hardcore
scientists might object to such calculations on the basis of non-verifiability, but this does
not refute the notion that such estimates are made using objective criteria and calculation.
Knight thus sees estimates an integral means of managing uncertainty and a highly
effective cognitive strategy for mapping the future.
For Knight, the only anomalous situation is the extent to which individuals thrive on
uncertainty and use it as a means of creative engagement in economic and social activity.
For Knight, it is the variegated responses to uncertainty that “accounts for a large part of
the phenomena of current economic life;” it is the gestalt that inspires the creation of
business systems, management processes and production ingenuities ─ all designed to
counter uncertainty.55 Uncertainty, in other words, drives serendipity and multifarious
outcomes through agential reactions to it. The profundity of this standpoint is not lost on
Knight; he recognizes it as marking the outer limits beyond which science and positivist
epistemologies lose their explanatory veracity. In the face of uncertainty, agential
authority made Knight profoundly skeptical of the idea of developing objective tools for
predicting human action or of developing methods of appraisal that would substantially
reduce the probability of error in calculating uncertainties.56 Agential interpretations of,
and reactions to uncertainty, combined with the myriad ways individuals seek to interface
53 R. A. Gonce (1992), “F.H. Knight on capitalism and Freedom,” Journal of Economic Issues, 26(3),
September, p.829.
54 Stephen F. LeRoy and Larry D. Singell (1987), op.cit., p. 397.
55 Frank Knight as quoted in R. A. Gonce (1992), op.cit, 829. See also the discussion in C. Robert Taylor
(2003), “The Role of Risk versus the Roe of Uncertainty in Economic Systems,” Agricultural Systems, 75,
pp.251-264.
56 A contrary interpretation to the one I have offered here concerning Knight’s distinction between risk and
uncertainty is provided by Richard M. Langlois and Metin M. Cosgel (1993), “Frank Knight on Risk,
Uncertainty, and the Firm: A New Interpretation,” Economic Inquiry, 31, July, pp.456-465.
27
with future situations before they materialize in order to alter the circumstances that
obtain, were altogether too complex a set of phenomena and too contingent on
interpretive discretion to be reduced to accurate calculation. Prediction, in other words,
was beyond the realm of the economist. The implications of this are profound. For
Knight, it delimited the ability of economics to become a science. Predicative accuracy or
the development of tools for the precise management of economic and social affairs
would forever remain beyond the reach of the economist. Economics, in Knight’s
understanding, was doomed to a theory-knowledge that could never adequately capture
the complexity of agential authority. As Ross Emmett observes;
The uncertainty created by the dilemmas and contradictions of subjective
knowledge and voluntary action in an open-ended universe means that neither
scientific nor probability analysis will provide the knowledge of individual action
or social consequences that the Economist craves.57
Knights tripartite classification of probability situations is summarized in Figure 2.1.
57 Ross B. Emmett, (1999), The Economist and the Entrepreneur: Modernist Impulses in Risk, Uncertainty
and Profit,” History of Political Economy, 31(1), p.41. See also John McKinney (1977), “Frank H. Knight
on Uncertainty and rational Action,” Southern Economic Journal, 43(4), April, pp.1442-1443; Stephen
John Nash (2003), “On Pragmatic Philosophy and Knightian Uncertainty,” Review of Social Economy,
86(2), June, pp.251-271.
28
Figure 1: Knight’s Tripartite Probability Classification
Probability
type
Characteristics
Examples of application and outcomes
in managing risk
A priori Probability
• Derived deductively
• Homogenous classification of
instances
• Defined universe of known
outcomes
• Can be objectively calculated
• Probability of risk recurrence can
be calculated using general
principles.
Games of chance with limited or defined
universe of possible outcomes (rolling of
a dice; tossing of a coin, ).
High level of certainty in terms of known
universe of outcomes but no certainty in
terms of the order and distribution of risks
29
Statistical Probability
• Derived inductively
• Rests on an empirical classification
of instances (statistical grouping)
• Generated through empirical
evaluation of relative frequencies
of a class of instances
• Universe of outcomes is less
defined but broadly enumerated
through recurrent observation /
tabulation of experience
• Homogeneity of instances
classified is subject to error and
the limitations of statistically
grouping dissimilar things
• Probabilities can be objectively
calculated
General forms of insurance such as life,
property, fire, and flood insurance
The universe of probable outcomes can be
reduced to broad classifications, but the
possible universe of outcomes can not be
fully known or the order and distribution
of risk outcomes known with certainty
30
Estimated Probability
• No valid basis of any kind for
classifying instances
• No ability to tabulate frequency of
instances because the instance is
unique or the frequency of
recurrence is extremely low
• Estimates are subjectively
generated
• Characterized by situations of
complexity and ambiguity
• The universe of outcomes can only
be anticipated on the basis of
instinct, inference and various
forms of deliberation and
subjective calculation
Many business, economic and life
instances where agents are required to
assess the likelihood of certain outcomes
materializing and or the possibility of
risks arising through various activities of
decisions taken by the agent (business
ventures, investment decisions, political
calculations, personal decisions, etc)
The universe of outcomes is uncertain and
cannot be known.
31
Assessing Knight’s Contributions to Theorizing Risk and Uncertainty
When John Maynard Keynes wrote that “it would be foolish, in forming our expectations,
to attach great weight to matters which are very uncertain,” Knight had reason to be
bemused. For Knight, Keynes misses the point when he states that “it is reasonable . . . to
be guided to a considerable degree by the facts about which we feel somewhat confident,
even though they may be less decisively relevant to the issue than other facts about which
our knowledge is vague and scanty.”58 But for Knight, herein lies the problem; that which
we know with some degree of certainty is mostly unimportant, not in the sense of its
objective value as knowledge, but in the sense that its ability to be imputed into
calculation and subject to scientific manipulation renders it intelligible. This quota of
knowledge is of course necessary, but the marginal quota. Of itself it reveals little. The
larger and thus more significant quota of knowledge rests in the realm of uncertainty. It is
in this environment that we formulate strategy, engage in exercises and actions calculated
through perceptions and inferences of an uncertain future. Understanding human action,
the composition, reflexive social landscapes and thus activities that propel change and
construct social and economic orders, or the decisions of firms and economic actors, rests
ultimately on understanding the role of uncertainty in rational action.59 It was here that
Knight made his greatest contributions to risk discourse. These contributions fall into
three categories.
58 John Maynard Keynes (1973), The General theory of Employment, Interest and Money. Macmillan &
Cambridge University Press for the Royal Economic Society, p.148. See also Mark Perlman and Charles R.
McCann, Jr. (1996), “Varieties of Uncertainty,” in Christian Schmidt (ed.), Uncertainty in Economic
Thought. Aldershot: Edward Elgar, pp.9-20. See also Oliver G. Wood, Jr., (1964), “Evolution of the
Concept of Risk,” The Journal of Risk and Insurance, 31(1), March, pp.83-91.
59 These Knightian propositions were not unproblematic. See, for example, T.W Hutchinson (1941), “The
Significance and Basic Postulates of Economic Theory: A Reply to Professor Knight,” The Journal of
Political Economy, 49(5), October, pp.732-750.
32
A Graduated Risk ─ Uncertainty Schema
The first is Knight’s development of an analytical framework that makes precise the
definitional parameters that separate risk from uncertainty. As Figure 2.2 highlights,
Knight essentially associates risk with a priori and statistical probability type situations
and uncertainty with estimated probability type situations. Apart from providing a
sophisticated heuristic device, Knight bestowed a powerful typology by which to
understand the role of risk and, more importantly, the mediums by which to assess the
likelihood of its recurrence. Knight’s conception of statistical probability and the
classification of instances rule, for example, laid the path for social scientists to develop
risk forecasting tools and for generating meaningful probabilities about the frequency of
risk generation. In no small measure, Knight gave social scientists a method for
quantifying risk.
33
Figure 2: Knightian Risk versus Uncertainty
RISK
a priori
probability
Universe of outcomes
can be nominally
defined and or
anticipated; ability to
control, prepare for or
mitigate the
consequences of
certain outcomes
Generated
by objective
assessment
Accurate measurement Seni-accurate measurement Less
accurate assessment
Quadrant 1
Statistical
probability
Uncertainty
Estimated
probability
No ability to anticipate
or define the universe
of possible outcomes;
ambiguous, complex,
indeterminate
Generated
by
subjective
assessment
Quadrant 2
Source: Adapted from Tony Lawson (1988), “Probability and Uncertainty in Economic
Analysis,” Journal of Post Keynesian Economics, 11(1), Fall, p.48.
34
Despite the heuristic utility of this graduated risk ─ uncertainty schema, however, Knight
paid little attention to enunciating what he identified as its most dominant element —
uncertainty. As a category supposedly endemic to all forms of human conduct, Knight
had little to say about possible modes of evaluation, mechanisms for its management and
or mitigation, or indeed the constitutive elements that comprise uncertainty. Instead,
Knight reifies the role of subjective evaluation and suggests implicitly that uncertainty
remains beyond rational deliberation; too complex and altogether too dyadic to be subject
to cognitive strategies that might meaningfully render this category more transparent or at
least able to be productively managed.
To what extent, however, is Knight’s conceptualization of uncertainty problematic? After
all, it constructs a kind of monolith that, by definition, renders it beyond science and
rationality and thus beyond a politics of control or a science of management and
mitigation. But how true is this? Should we simply accept Knight’s definitional construct
and defer to its implications? To put it another way, we perhaps need to inquire how
uncertain uncertainty actually is? Knight has a graduated scale from highly probabilistic
risks calculated through a prior probability analysis to a category that he defines as
beyond all forms of anticipation; events so unique, instances so exceptional that they
essentially fall outside of our ability to comprehend them. Surely, however, this category
speaks not to a broad and encompassing category true of much of the phenomena that we
experience in everyday life, but a rare category that many if not most of us never
encounter in everyday life? We live, for example, mostly in highly institutionalized
environments mediated by rules, laws, and norms, all of which create path dependencies
and a larger degree of certainty than uncertainty. And while, to be sure, the degree of
social embeddedness of formal and informal institutional practices varies greatly between
and within national communities, the cultural and social norms that arise from these
contexts act to produce customs, practices and regularities all of which reduce
uncertainties. In broader social contexts, then, to what extent is it valid to talk about the
role of uncertainty dominating social life? Life patterns in industrialized countries and
increasingly in large parts of Asia and Latin America, for example, display greater
35
regularities than has historically been the case. Institutionalized statist approaches to the
management of welfare, living conditions, health care provision and educational
attainment, while they do not eradicate uncertainty increasingly produce greater
regularities and stability in our life patterns and the likely trajectories we will each
experience.
Equally, if we consider the economic and business worlds to which Knight devotes much
of his enterprise, these same predicates would seem to apply. The extension and
normalization of property rights, contract law, and the increasing degree to which
investor rights are now subject to internationally uniform standards, surely introduce less
and not more uncertainty? The emergence of the regulatory state and the codification of
business law, market operation, securities law and the compliance requirements for
reporting standards and corporate transparency, obviously do not eradiate uncertainty in
terms of business failure, corruption, or poor management, but they increasingly
regularize business operations and business practices which help reduce the prospectus
for happenstance.60 Knight, of course, could not have anticipated the depth of the
regulatory state or the great extension of institutionalized practices into the business and
social world. The point, however, is that such practices perhaps change the categorical
realm of uncertainty and reduce its visceral impact on the collective body politic. In other
words, uncertainty might be increasingly less uncertain and the modes of analysis,
institutional and regulatory practices that we bring to bear upon it, progressively
subjugating uncertainty to processes that circumscribe its consequences. This is not a
process of regulating uncertainty away, but an increasing ability to manage its
consequences.
Second, to what extent is uncertainty categorically unique such that it is impossible to
map, anticipate, or mitigate? Knight implicitly suggests that the application of science,
measurement, or reliable calculation are not possible in situations of uncertainty. But how
60 See, for example, David Levi-Faur and Jacint Jordana (2005), “The Rise of Regulatory Capitalism: The
Global Diffusion of a New Order,” The ANNALS of the American Academy of Political and Social
Sciences, 598(1), pp. 200-217.
36
true is this? There is, after all, a vast array of cognitive mapping strategies that exist in
various disciplinary and professional settings aimed at helping manage uncertainty.
Scenario analysis, for example, is hardly new and an integral part of agential reasoning
and cognition. History is littered with such thought examples, from military strategists
recounting the Peloponnesian wars and the scenarios played out by the Athenians against
the Peloponnesian League, to the assault of Napoleon on Russia and Czar Alexander I
and the intricate scenarios of Marshal Kutuzov as he campaigned against the invading
French. These same cognitive strategies were famously employed by business and the
great industrial barons of the nineteenth and twentieth century with great effect. Andrew
Carnegie, John Pierpont Morgan, Henry Ford and John D. Rockefeller, among others,
were all masters at anticipating competitor behavior and mapping their business moves in
much the same way that a chess player would map-out the alternative options of an
opponent in order to presage their own moves and options. Whether formally mapped or
cogitatively implicit, the point is that scenario analysis has proven a powerful tool in the
management of uncertainty.
Similarly, the development and application of trend analysis techniques to social,
political and economic phenomena, the use of path dependency analysis to map the
trajectory of institutional forms, norms and practices of discrete segments of populations,
or the use of Delphi techniques in the generation of political and commercial forecasts,
have all emerged as key tools for managing situations of uncertainty. Indeed, converting
uncertainty into manageable risks has been the hallmark of disaster and emergency
planning. “Over the horizon” mapping strategies have been applied to all manner of
crises and disaster planning; improving our capacity to manage and mitigate the effects of
earthquakes, hurricanes, floods, various weather related events, criminal activities and
terrorist attacks ─ to name but a few. Hardening critical infrastructure, securing
continuity of supply in the case of critical resources, redundancy-back-up planning in the
case of water supply, or emergency management protocols in the case of critical
infrastructure failures, are all common preparedness protocols now widely adopted
37
among professional agencies with proven ability to manage uncertainty and mitigate the
impact of relatively infrequent events.
Uncertainty might thus not be the black hole that Knight paints it, but a category where
effective management protocols can be developed to reduce its effect and anticipate its
consequences.
All of this does not repudiate Knight’s risk ─ uncertainty schema, but it does make
problematic his conceptualization of uncertainty as a category which both dominates our
lives and one which must forever remain enigmatic. It also raises questions about the
ontological efficacy of conceiving of uncertainty as a realm of danger and or harm; a
great unfathomable realm. Knight was not alone in his thinking. Despite their strong
disagreements, John Maynard Keynes shared his critic’s pessimism, suggesting that in
matters such as whether there will be war or whether stock prices would rise or fall,
“there is no scientific basis on which to form any calculable probability whatsoever. We
simply do not know.”61 In the absence of a rational, scientific calculus, uncertainty
remained a dark and negative force. But is it? As Bernstein notes, “uncertainty makes us
free.”62 Uncertainty liberates us from a teleology of historical determinism, of the grand
designs of history as an ultimate trajectory manipulated by gods, men and
megalomaniacs. Uncertainty democratizes our collective destinies. The negative or dark
view of uncertainty as “danger” or “harm” perhaps, then, reflects a cultural anguish
peculiar to techno-managerialists and the modernist project of constructing architectures
of social and economic control rather than an objective assessment of the condition itself.
The foot soldiers of science celebrate order, patterns, causalities, and parsimonious
models able to relate cause to effect, inputs with outcomes, triggers that instigate
processes, and the phenomena that result. Uncertainty, by contrast, invokes notions of
disorder, an absence of control, an inability to correlate causalities, to predict and
manage. It is pre-science, pre-modern and primordial. As a man of science and a
61 John Maynard Keynes as quoted in Pat O’Malley (2004), Risk, Uncertainty and Government. Bodmin,
UK.: Lasshouse Press, p.4.
62 Peter Bernstein as quoted in ibid., p.4.
38
celebrant of rationalist approaches, Knight thus held contradictory views about the
composition of uncertainty, seeing it as omnipresent and thus a black hole beyond
rationality, but also the quotient responsible for creativity, innovation and progress.
A Risk Discourse for Social Science
Knight’s second great contribution laid in the risk research agenda he bequeathed to
social science. Knight essentially pointed the way forward in terms of detailed,
contextualized historical studies of commercial processes and institutions and then
tabulating the risk events associated with these to produce risk maps in terms of statistical
probabilities. Knight essentially championed a method of analysis that, if followed,
would allow relatively high degrees of accuracy in anticipating risk events within discrete
classes of instances. Experiential tabulation combined with the classification of instances
rule, in other words, provided the one concrete social scientific tool that could garner
insights not just into the frequency of risk events, but the contours of their severity, the
nature of their impact and the resultant implications for the organization / populations
concerned.
Strangely, however, the up-take of Knight’s approach has been sparse outside of actuarial
studies and certain facets of sociology, social work, medicine and public health. Social
work and public health studies, for example, have used the classification of instances
approach as a means of developing diagnostic tools and intervention strategies for
identifying individuals / populations at risk. Indeed, public health campaigns rest on this
approach, developing increasingly intricate classifications of traits and exigent factors
that are correlated closely to major health events (smoking and heart disease; sedentary
lifestyles and cardiovascular disease; blood pressures and chronic cardiovascular events
such as stroke and heart attack, etc.). Similarly, in social work the classification of
instances method is variously used to identify youth at risk of suicide, the frequency and
distribution of family breakdown, and as a basis for the development of prescriptive
policy to enhance the delivery of social services to communities with high propensities
39
for risk events (family violence and spousal abuse, child truancy, etc). In each of these
areas, the intensive development of rigid classifications and the investment of
considerable resources into tabulating the frequency of risk events through constructing
longitudinal time-series data has revolutionized public health and social work
methodologies, diagnostic tools and policy mechanisms to manage and help mitigate
public health and social risk. With such demonstrably superior outcomes the question
arises, however, why more of the social sciences have not emulated this approach?
Part of the explanation for the poor uptake of similar approaches in political science and
International Relations (IR) undoubtedly lies in the propensity for rational choice
methods and game theoretic approaches, which, ironically, have precluded the type of
onerous data gathering necessary to fruitfully develop classifications and statistical
probabilities. So too, part of the problem also lies in the historical bias against “grubby
empiricism;” grand theoretical approaches and meta-narratives are much the more
fashionable and bring professional notoriety. Correcting professional incentive structures
is obviously no easy task. Yet, if political science and international relations are to make
significant inroads into developing more robust risk assessment tools and, concomitantly,
developing the types of interventionist risk mitigation strategies indicative of various
health sciences and social work, some form of emulation might be necessary and
desirable. As political scientists, for example, we tend not to map the phenomena we
profess to study. Political risk analysis, for instance, tends to be defined by ad-hoc
approaches relying on due diligence check lists, situation analyses or case study
approaches. There is no data to mine; no longitudinal time-series data that might allow
various classifications of risk (regulatory risk, contract repudiation, policy change,
expropriation, etc) to be correlated to institutional type or to specific institutional features
such as accountability mechanisms, transparency, probity, institutional capacity, statutory
independence, budget procurement practices, etc. In political science we simply don’t
have the empirical data sets to map the associations that obtain between certain types or
features of institutions and various forms of political risk and thus calculate the
40
probability of such risks recurring.63 Doing so would yield significant insights into
political risk, providing not only a means of calculating the propensity for risks in
specific institutional settings but, more importantly, helping to define policy prescriptions
that redress the institutional design flaws that allow risk generation in the first place.
Knight’s legacy might thus lay in emulating his method and allowing it to provide a
catalyst to help develop and systemize a more rigorous theoretical basis for risk
assessment.
The Limits of Knowledge
Knight’s third and obvious contribution was perhaps his greatest: demarcating the
possible from the improbable. In differentiating between risk and uncertainty, Knight
explicitly suggested the limits of knowledge and the practical limitations of what can be
controlled and or reasonably calculated (quadrant 2, Figure 2.2). Knight was a rationalist
and believed strongly in the role of rationality as a medium of scientific inquiry. But he
was not a scientist nor did he believe economics or any other social science could become
a “science” as might be true of physics or chemistry. Causality and the generative drivers
of human action were simply too interdependent, too complex and far too emotive in
terms of subjective calculation to make them amendable to general laws or principles.64
As a consequence, the role of the social scientist, in Knight’s view, could not be to
“discover truth” since much of this was socially fabricated, but to champion social justice
63 See Paul Dragos Aligica (2006), “Institutional & Stakeholder Mapping: Frameworks for Policy Analysis
and Institutional Change,” Public Organization Review, 6, pp.79-90. J. Roger Hollingsworth (2000),
“Doing Institutional Analysis: Implications for the Study of Innovations,” Review of International Political
Economy, 7(4), Winter, pp.595-644. Notable and fascinating contributions to empirical – institutional
mapping exercises can be found in Thorsten Beck et al (2000), “New Tools and New Tests in Comparative
Political Economy: The Databases of Political Institutions,” Development Research Group, World Bank,
Washington, D.C. Working Paper & Yi Feng (2001), “Political Freedom, Political Instability And Policy
Uncertainty: A Study of Political Institutions and Private Investment in Developing Countries,”
International Studies Quarterly, 45, pp.271-294.
64 John Nash (1998), Cost, Uncertainty, and Welfare: Frank Knights Theory of Imperfect Competition.
Aldershot: Ashgate, p.59. See also Frank H. Knight (1960), Intelligence and Democratic Action.
Cambridge, Massachusetts: Harvard University Press; Frank H. Knight (1947), Freedom and Reform:
Essays in Economics and Social Philosophy. New York: Harper & Brothers;
41
and advocate ways for improving social outcomes.65 Economics, in other words, was to
be purposive. 66 As John McKinney notes, Knight’s concern was to “keep science within
an appropriately restricted domain” while simultaneously using science “to develop a
rigorously mechanical interpretation of human conduct, and then as a social philosopher
and moralist, impress upon his readers the ‘sweeping limitations’ which must be placed
on such an interpretation.”!67 Those limitations, of course, disposed Knight to place
considerable analytical weight on the role that uncertainty plays in every facet of
economic and social life, and to admit that this categorical realm spoke to the limits of
knowledge but, at the same time, should define its concerns, focus and energies. As Ross
Emmett notes of Knight’s RUP, its implications are profound. “For the modern
economist, bent on pursuing the science of economics to its limits, RUP presents a
cognitive tragedy: in an open-ended universe, ‘the essential evil of uncertainty’ is the
impossibility of complete knowledge.”68
The ramifications of Knight’s work thus remain as pertinent today as when first
published in 1921. It is, perhaps, in this light that we should mine Knight’s contributions;
exploiting his keen insights into the analytical distinction between risk and uncertainty,
mining his methods as a means developing more sophisticated tools for assessing risk
through the calculation of statistical probabilities, but informed ultimately by the
limitations of what we can reasonably expect to achieve under the weight of uncertainty.
65 Arther Schweitzer (1992), “Frank Knight’s Social Economics,” in Mark Blaug (ed.) Pioneers in
Economics: Frank Knight (1885-1972, Henry Simons (1899-1976), Joseph Schumpter (1883-1950).
Aldershot: Edward Elgar, p.35.
66 Indeed, Mary S. Morgan suggests that Knight’s thinking was so antithetical to notions of economic
science and specifically to economistic abstractions like “economic man” that “Knight insisted that this
ideal figure of economic science does not help describe actual economic behavior, and so cannot be used
for socially useful economic analysis or policy interventions. See Mary S. Morgan (2006), “Economic Man
as a Model man: Ideal Types, Idealization and Caricatures,” Journal of the History of Economic Thought,
28(1), March, p.15.
67 John McKinney (1977), Frank H. Knight on Uncertainty and Rational Action,” Southern Economic
Journal, 43(4), April, pp. 1439-1440.
68 Ross B. Emmett (1999), The Economist and the Entrepreneur: Modernist Impulses in Risk, Uncertainty
and profit,” History of Political Economy, 31(1), pp.31.
42
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