Herbert Simon講座系列#19

Behavioral Economics & Experimental Economics

行為經濟學與實驗經濟學

http://www.aiecon.org/herbertsimon/

自2002年諾貝爾奬頒給了Vernon Smith 和Daniel Kahneman之後，行為經濟學和實驗經濟學，就佔了與新古典經濟學時而為互補、時而為替代的重要地位。而Herbert Simon 在行為經濟學發展之初，雖佔有極大的貢獻，但其影響卻在晚近常被忽略或被錯誤的詮釋。Prof. Kumaraswamy (Vela) Velupillai 乃跨計算經濟學及行為 經濟學二領域之大師，他於以下兩場演講中，除了介紹當前行為經濟學的發展外，並會介紹Herbert Simon 的行為經濟學（Prof. Velupillai 稱之為古典行為經濟學）和當代行為經濟學間的關係，這是放諸目前文獻所少見的內容。

Prof. Velupillai 在介紹古典行為經濟學中，會特別將Herbert Simon 的思想啟蒙與計算理論之父 Alan Turin 的傳承建構起來，並進而闡述古典行為經濟學中的可計算（Computable） 基礎。至於可計算經濟 學與行為經濟學及實驗經濟學間的關係，則將在Prof. Stephen Kinsella 的兩場演講中延續。

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議程Program Schedule:

主辦單位Sponsor：國立政治大學經濟系(Economics Department, National Chengchi University)

協辦單位 Co-sponsors：國立政治大學頂大辦公室、國家科學委員會 (Top University Program of National Chengchi University, National Science Council)

摘要 Abstracts:

 Prof. Kumaraswamy (Vela) Velupillai

“There are many levels of complexity in problems, and corresponding boundaries between them. Turing computability is an outer boundary, and as you show, any theory that requires more power than that surely is irrelevant to any useful definition of human rationality.”

Letter from Herbert Simon to Vela Velupillai, 25 May, 2000

Lecture I: Reviving the Simon Tradition in Behavioural Economics (Handout)

No one person combined and encapsulated, in an intrinsically dynamic framework, a computationally founded^§ theoretical system of choice and decision, both entirely rational in a broad sense, than Herbert Simon. In this lecture I try, in fairly precise and formal ways, to suggest computable foundations for boundedly rational choice and satisficing decisions. In a nutshell, the aim is to reformulate, with textual support from Herbert Simon's characterizations and suggestions, bounded rationality and satisficing in a computable framework so that their intrinsic complex dynamics is made explicit in as straightforward a way as possible. To achieve this aim, in the tradition of Simon, I start from orthodox underpinnings of rational choice theory and extract its inherent procedural content, which is usually submerged in the inappropriate mathematics of standard real analysis.

    In his fascinating and, indeed, provocative and challenging article titled, What is Bounded Rationality?^ª, Reinhard Selten first wonders what bounded rationality is, and then goes on to state that an answer to the question `cannot be given' now:

"What is bounded rationality? A complete answer to this question cannot be given at the present state of the art. However, empirical findings put limits to the concept and indicate in which direction further inquiry should go."

     In a definitive sense - entirely consistent with the computational underpinnings Simon always sought - I try to give a `complete answer' to Selten's finessed question. I go further and would like to claim that the `limits to the concept' derived from current `empirical findings' cannot point the direction Simon would have endorsed for `further inquiry' to go - simply because current frameworks are devoid of the computable underpinnings that were the hallmark of Simon's behavioural economics.

Lecture II: Behavioural Economics: Classical and Modern (Handout)

I begin this lecture with an explication of the following three fundamental theorems of classical computability theory, computable analysis and real analysis, respectively:

1.      The Blum Speedup Theorem

2.      Specker’s Theorem

3.      The Bolzano-Weierstrass Theorem

I use these three classic theorems, and their explicit and implicit invocations in varieties of mathematical economics, to motivate a discussion of the fundamental difference between classical and modern behavioural economics.

With this motivation as a backdrop, I next characterize the formal difference between classical and modern behavioural economics in terms of the difference between decision problems and optimization problems.

Assuming familiarity with, if not also complete knowledge of, the formal mechanisms and analytics of optimization theory – in all its many splendours, including game theoretic and in all the standard varieties of dynamic settings – I concentrate, next, on defining and explaining the nature, scope and formal machinery underpinning decision problems. This leads, almost naturally, to a consideration of complexity classes of decision problems and, therefore, underpins the natural setting in which bounded rationality, satisficing and problem solving – and problem solvers – become the foundations on which classical behavioural economics was erected, almost single-handedly by Herbert Simon.

This is contrasted with the framework and mathematical foundations of modern behavioural economics, which remains within the fold of variations of optimization theory, which is – and, indeed, can be shown to be – a special case of decision problems.

Brief remarks on algorithmic probability theory as a foundation for classical behavioural economics and the contrast with classical – either varieties of subjective or measure-theoretic – probability theory underpinning modern behavioural economics, conclude this lecture.

Prof. Stephen Kinsella

Abstract 1: Experimental Recipes (Handout)

This first talk introduces students to experimental economics in a
practical way: we will view the creation of an economic experiment
like a short-order cook views a meal: as the creation of a simple
series of steps combined in a certain order. Later, we’ll fill in the
theoretical blanks we left behind.
Experiments in economics have gained currency in the last 40 years,
culminating in the award of the ‘Nobel’ prize in economics to Vernon
Smith in 2002. More distinguished experimenters are sure to receive
the prize in coming years. The AIECON lab has world-renowned expertise
in computational intelligence. Experimental studies of intelligence,
broadly defined, and in a Simonian sense, will serve to bolster and
augment the research currently being undertaken at the lab.
This talk introduces students to the planning and running of a real
world experiment. We’ll define terms as we go. The object of the first
lecture is to be as ‘hands on’ as possible with the material.

Abstract 2: The 'Computable' in experimental economics (Handout) An_example

Computable economics is the natural theoretical underpinning for
modern experimental economics: this lecture delves into the history of
experimental economics, and suggests points of tangency and fruitful
convergences between computable economics and experimental economics.
A road map for computable and experimental economics---linked
fundamentally to computational intelligence---is given.