ECONOMICS 606,

NEW TRENDS IN ECONOMIC THEORY,

W.A. BROCK, SPRING, 2006

Since I did not teach 606 last year and since the research frontier has

changed, this year's 606 will try to get you up to date on major new research

trends in economics. Besides trying to identify "cutting edge" research

topics in economics and trying to identify good PhD thesis topics, I try to

use 606 to teach basic tools that are hard to get elsewhere. Hence, if you

have had 606 before, and you would like to take this course for credit, even

though you may have "taken it before," I will sign the necessary paperwork

to do this for you.

On the first day of class I will sketch different potential areas of

teaching concentration listed in the syllabus below and hand out 3x5 cards

where each student is asked to identify areas of interest that they would like

me to teach. I will also ask students to tell me what courses they have taken

on these cards. I will then try to design the course to optimize a combination

reflected by the preferences and backgrounds revealed on these cards. I will

also try to link the teaching to research projects of faculty here.

I will list some recent and current UW PhD students who have gotten PhD

theses (or are currently working on PhD theses) out of the areas to be taught

below. You will want to talk to the ones who are currently here.

The topics below seem unrelated. I shall show how a few analytical

principles organize the lot. There will be some similarity between the

material of previous 606 courses but 606 Spring, 2006 will contain much more

work on "new" decision theory including different criteria for "optimization"

when underlying structure is partially known. This area is sometimes called

"Decision making under ambiguity." This area includes the following items and

ideas: Bayesian model averaging, maximin decision making, minimax regret

decision making, design limits, etc. Writers in the area include Larry

Epstein, Charles Manski, Lars Hansen, Thomas Sargent, Itzhak Gilboa, David

Schmeidler, and some faculty here. Key references to work outside UW are

located on the websites of Lars Hansen, Thomas Sargent, Larry Epstein, and

Charles Manski.

We shall also discuss recent econometric approaches to the analysis of

time series data and of panel data in an attempt to separate "spurious"

"spatial" and temporal dependencies from "true" dependencies that have some

notion of "multiplier." Documentation of existence of "multipliers" is

important for policy analysis because multipliers are associated with

externalities which policy may be able to correct.

There will also be more attention paid to the relationship between

dynamical systems phenomena such as bifurcations and jumps caused by presence

of "multipliers" to assist identification of such "endogenous interactions"

than recent work on identification of self selection effects and treatment

effects which was treated in years before. This will be a blending of

stochastic dynamical systems approaches with the review article,

"Interactions-Based Models," for the HANDBOOK OF ECONOMETRICS by Brock and

Durlauf.

We will spend some time on very recent work in econometrics on "early

warning signals" of an impending bifurcation in settings where the time scales

can be separated. Rigorous treatment of the notion of "separation of time

scales" will be developed in the course.

Researchers such as N. Bockstael of University of Maryland and E. Irwin

of Ohio State (see the dramatic maps generated by simulated urban/suburban

interacting systems models compared with actual patterns on E. Irwin's website

at Ohio State) have recently taken interactive systems models towards exciting

empirical applications. We shall cover some of this new work.

The topics below have become very popular, not only because of their

intrinsic interest, but also because of the recent entry of

"establishment" figures. The popularity is projected to increase even more

due to recent empirical applications to issues of high political salience such

as the control of urban sprawl. At the risk of repeating, the purpose of this

course is to bring our students to the research frontier as well as to inform

our students of recent empirical applications as well as to suggest open

research problems.

GRADING

This course is designed to help students locate and identify potential

thesis topics as well as to teach basic analytical tools. I believe that

students learn analytical tools and techniques much faster when applications

that lead to potential thesis topics are used as the main pedagogical

vehicles. Hence, I will base grades on a take home test and problem sets.

Students will be given a week to do the take home test. The problems on the

test and homeworks will be "toy" research problems that apply ideas taught in

the course. The take home test will be designed so that those students who

faithfully do their homework problem sets will almost surely do well on the

take home test. The key idea here is to help students adjust from the usual

type of course environment to the research environment.

OVERVIEW AND MORE DETAILS

Since much of the material that I have taught before in this course is

available elsewhere (Examples: Dynamic Programming is taught in first year

macroeconomics, Stochastic Calculus and Stochastic Optimal Control Theory is

taught in the Business School and the Math Department, Game Theory is taught

by other courses here) I keep revamping this course to teach material that

is not so easily available elsewhere on campus. I will teach newer

methods that have become popular in recent years. I list these methods and

topics below.

The emphasis in teaching the methods will be to isolate potential PhD

thesis topics. More will be said about this below.

Some major writers and/or sources are included in parentheses beneath

each topic. However, these will be very incomplete because, during the

course, I shall make up lists of current papers and their website locations in

order to build the good research habit of drawing up a list of high priority

websites and the habit of continuously monitoring these in order to keep up in

today's fast moving research environment.

LEVEL AND OVERVIEW OF DESIGN OF THIS COURSE

This course will be accessible to students with preparation at the level

of "mature" first year graduate students in economics and business. Hence, I

am choosing a mathematical level to widen the accessibility of the course

compared to the past. I will also attempt to make the course accessible

(and relevant by using applications in the following areas if students in

those areas enroll in the course) to students in other disciplines such as

statistics, physics, biology, ecology, limnology, etc.

Since much of the material for the course consists of current working

papers as well as many published papers, I will make much use of websites and

other internet resources. I shall teach my own favorite internet search

methods for framing a research topic and projecting potential value-added of

the proposed topic before investing time on it. While this might seem banal,

it is surprising how many people fail to do this and end up re-inventing the

wheel. Students here have too many time committments to have their research

time wasted on projects that have already been done by someone else.

This course will represent a good hunting ground for potential thesis

topics because the new methods can be applied to many different areas of

economics, finance, and other areas including ecology. I shall try to outline

potential thesis topics during the lectures. For other students, the course

will give a tour of some interesting scenery on the research frontier of

economics and related areas such as ecology. The course is also designed to

be useful to advanced undergraduate students that are contemplating academic

research careers.

The unifying concepts and tools of the course will be: (i) stochastic

dynamical systems theory, (ii) decision theory in settings of partially known

or partially identified structures. Emphasis will be placed on

data-disciplined approaches. (iii) theories of self-organization, agent-based

modelling, scaling laws, evolutionary dynamics with many agent types,

including theories of the Santa Fe Institute variety, and some of the tools

being used by papers posted on websites that can be found by googling on

"CeNDEF" (Center for Nonlinear Dynamics, University of Amsterdam),

"econophysics," "the New England Complex Systems Institute," and the like.

(iv) econometric methods that stress heterogeneity and detection of regime

changes, e.g. bifurcations, caused by slow moving underlying variables.

I will also teach recent econometric and statistical methods of dealing

with separation of time scales, construction of early warning indicators of

impending bifurcations. Interesting material may be found by simply doing a

google search on the words "econophysics," "complex systems," "genetic

algorithms," "bifurcations", and other jargon words from "complexity theory."

The Santa Fe Institute website and CeNDEF website

http://www.santafe.edu; http://www.fee.uva.nl/cendef/

are good places to start looking at complex systems materials. We will use a

lot of materials on the CeNDEF website above.

I use applications heavily in my teaching of analytical tools and

techniques. For UW applications of complex adaptive systems ideas and related

ideas to ecological economics, see the following SSRI working papers:

W. D. Dechert and S. I. O'Donnell, 2005-19; S. Carpenter and W. Brock,

2005-18; W. Brock, S. Carpenter, and M. Scheffer, 2005-14; D. Ludwig, W.

Brock, and S.Carpenter 2005-15.

For some mathematical tools on bifurcation analysis, separation of time

scales, and their applications, see, Brock, W., "Some Mathematical Tools for

Analyzing Complex-Nonlinear Systems," SSRI W.P. 2020.

SSRI Working Papers are available on the Sixth Floor in the SSRI Office.

They are also available at the SSRI website where you can download them.

AN OVERVIEW OF POTENTIAL TOPICS

We will cover topics below in order of I,II,...Hence we may run out of

time before we cover all the topics listed here. But I will try to give you

at least a taste of all of them.

I. Bayesian Model Averaging, Robust Control, Design Limits in control theory,

Decision Making Under Ambiguity, and other methods of appraising and dealing

with decision making in contexts of partially identified structures. Several

UW students have gotten thesis topics out of this area, but I believe the

frontier is still wide open.

The articles on robust control and the book, ROBUST CONTROL AND MODEL

UNCERTAINTY IN MACROECONOMICS by Lars Peter Hansen and Thomas Sargent and

their coworkers (available on Sargent's website at NYU) has stimulated a lot

of recent interest. Get to Sargent's website at NYU by googling on "Thomas J.

Sargent." We shall cover the highlights of this work as well as related work

being done here.

Recent UW work in the area is in SSRI working papers by Brock and

Durlauf, Brock, Durlauf, and West. Some very recent UW theses in this area

include Oya Ardic (now at University of the Bosphorus), Chi Ming Tan (now at

Tufts), Andros Kourtellos (now at University of Cyprus), Sibel Sirakaya (now

at University of Washington, Seattle).

Mehmet Eris, Giacomo Rondina, and Ethan Cohen-Cole, are some current UW

PhD students working in this area.

For example the papers, "Policy Evaluation in Uncertain Economic

Environments, by Brock, Durlauf, and West (SSRI 2003-15), as well as "Growth

Economics and Reality" by Brock and Durlauf (available at the SSRI office and

website) review this area and applies it to growth econometrics and monetary

economics. The SSRI working paper, 2004-21, by Brock, "Profiling problems

under partially identified structures" discusses decision making under minimax

regret, maximin decision making and adaptive learning under these two

criteria. This paper can serve as an introduction to some economic

applications of general decision making and adaptive learning under ambiguity

where the ambiguity (i.e. the level of partial identification) is disciplined

by data. Key references are papers by Charles Manski which are located on

Charles Manski's website.

We will discuss some of these methods and potential applications for them

in the course.

II. Recent econometric and theoretical modelling of increasing

returns, threshold effects, interaction effects, and theories of endogenous

emergence of spatial patterns.

We will teach some of the latest work of this genre. Some illustrative

references are given below.

(W. Brock and A. Xepapadeas, "Optimal control and spatial heterogeneity:

Pattern formation in economic-ecological models," (SSRI 2005-11); The three

Santa Fe Institute Volumes by Anderson, Arrow, Pines, (1988), (Arthur,

Durlauf, and Lane) [Blume and Durlauf], THE ECONOMY AS AN EVOLVING COMPLEX

SYSTEM, (II), [III] Addison Wesley: Redwood City, CA. [Oxford University

Press]; Brock, W., (1993), "Pathways to Randomness in the Economy: Emergent

Nonlinearity and Chaos in Economics and Finance," SSRI Reprint 410; Brock,

W., (1991), "Understanding Macroeconomic Time Series using Complex Systems

Theory," SSRI Reprint 392. Manski, C., (1993), "Identification Problems

in the Social Sciences," SSRI Reprint 409. Manski, C., "Dynamic Choice in

Social Settings," SSRI Reprint 408.

Main sources of recent work that was stimulated by the economics

program at the Santa Fe Institute are the second and third SFI Volumes:

Arthur, Durlauf, and Lane, eds., (1997), [Blume and Durlauf (2005)] THE

ECONOMY AS AN EVOLVING COMPLEX SYSTEM II, [III] Addison Wesley: Redwood City,

CA. [Oxford University Press]. The review by Brock and Durlauf

"Interactions-Based Models," SSRI W.P. 9910 contains much material on

econometrics of social interactions.)

III. Neural Nets, Connectionist Networks, Bootstrapping, Surrogate Data

and their relationship to other received methods in econometrics such as

nonlinear least squares.

Some relevant references are below.

(Sullivan, A. Timmerman, and Halbert White, (1998), "Data-Snooping, Technical

Trading Rule Performance and the Bootstrap," Department of Economics, UCSD and

LSE Finance), Casdagli, M., Eubank, S., (1990), NONLINEAR MODELLING AND

FORECASTING, Addison-Welsey: Redwood City, CA. Work of Halbert White and his

students at UCSD on estimating neural nets using "Robbins-Monro" procedures

and nonlinear least squares. Similar methods are used in the adaptive

learning literature below.)

This year's 606 will also discuss the problem of controlling for

data-snooping in econometric methodology in general as well as in the

application of bootstrap-based specification tests (cf. Sullivan, Timmerman,

and White). See also the discussion below on how this material will be taught

and used this year.

IV. Self Organized Criticality Models

Some references are listed below.

(Bak, P., Chen, K., Scheinkman, J., Woodford, M., (1993), RICHERCHE

ECONOMICHE. Krugman, P., (1995), THE SELF ORGANIZING ECONOMY. Purpose:

Attempt to explain the evidence for long dependence in economic and financial

data stressed by Mandelbrot and others. Recent articles on Self Organized

Criticality (SOC) that are mentioned in Per Bak's book, HOW NATURE WORKS,

Springer 1996 will also be covered. See also the ECONOPHYSICS website for

much material on SOC applied to economics. See especially the joint work of

Martin Shubik of Yale Economics with Per Bak of Physics.)

I will review some high points of this type of work posted on

ECONOPHYSICS websites as well as other related websites such as the Santa Fe

Institute. We will discuss how we might use this kind of material to advance

the frontiers of econometrics and theory in economics.

V. Econometric and Theoretical issues raised by the possible presence of

chaos and other forms of deep nonlinearity in economic and financial data.

The Problem of Detecting "Spurious" Nonlinearity in Data.

Some references are listed below.

(NONLINEAR DYNAMICS AND ECONOMETRICS SPECIAL ISSUE: JOURNAL OF APPLIED

ECONOMETRICS, December, 1992. Barnett, W., Gallant, A., Hinich, M.,

Jungeilges, J., Kaplan, D., Jensen, M., "A Single-Blind Controlled Competition

between Tests for Nonlinearity and Chaos," Washington University, St. Louis

working paper. See William Barnett's website at the University of Kansas,

Lawrence, Kansas, for many interesting papers and well as useful links. Some

interesting books are Benhabib J., ed., (1992), CYCLES AND CHAOS IN ECONOMIC

EQUILIBRIUM, Princeton University Press: Princeton, NJ. Brock, W., Hsieh, D.,

LeBaron, B., (1991), NONLINEAR DYNAMICS, CHAOS, AND INSTABILITY: STATISTICAL

THEORY AND ECONOMIC EVIDENCE, MIT Press: Cambridge, MA. Granger, C.,

Terasvirta, T., (1993), MODELLING NONLINEAR ECONOMIC RELATIONSHIPS, Oxford

University Press: Oxford. De Grauwe, P., Dewachter, H., Embrechts, M.,

(1993), EXCHANGE RATE THEORY: CHAOTIC MODELS OF FOREIGN EXCHANGE RATES, Basil

Blackwell: Oxford. A challenge to this literature is posed by Bickel and

Buhlmann, (1996) "What is a Linear Process?" PROC.NAT. ACAD. SCI. USA, Vol.

93, pp. 12128-12131, December. BB argue that the closure of the set of ARMA

processes "under a suitable metric" is "unexpectedly large" (Caution: This is

NOT the Wold representation). Further work on this problem should be at Peter

Bickel (Berkeley Statistics) and Peter Buhlmann's websites. The CeNDEF

website has lots of material on this topic. We will relate this type of work

to work by UW's Bruce Hansen on econometrics of nonlinear models, UW's Dennis

Kristensen on econometrics of diffusion processes, and other work at the UW.)

This area has grown rapidly. I shall pick highlights, teach the basics,

and show what still needs to be done. New work that has become available

recently will be covered. The emphasis will be to inform students on what

research problems are still open in this area and how it relates to the recent

surge of interest in modelling "bounded rationality" and "process approaches"

to economics rather than "equilibrium" approaches.

VI. Complex Systems Modelling and Scaling "Laws"

Some references are listed below.

(Stein, D., (1988), ed., LECTURES ON THE SCIENCES OF COMPLEXITY,

Addison-Wesley: Redwood City, CA. Brock, W., "Scaling in Economics: A

Reader's Guide," SSRI Reprint. Blake LeBaron's website at Brandeis

(http://stanley.feldberg.brandeis.edu/~blebaron)

LeBaron, B., 1999, "Volatility Persistence and Apparent Scaling Laws in

Finance," (available at LeBaron's website).

Browse the ECONOPHYSICS website (see especially the links to "minority

games."))

Here are examples of Scaling "Laws" in economics and finance: (i)

Gibrat's Law of firm size distribution, (ii) logistic "laws" of growth and

diffusion, (iii) Pareto's Law of income distribution, (iv) Mandelbrot's

"self similar" stochastic processes and "1/f" scaling in economics and

finance, (v) the stylized facts of finance such as autocorrelation

structure of returns, volatility measures, and volume measures across

individual stocks and indices, (vi) the stylized autocorrelation and

cross correlation structure of aggregative and less aggregated

macroeconomic time series. See Brock, W., and LeBaron, B., "A dynamic

structural model for stock return volatility and trading volume," REVIEW OF

ECONOMICS AND STATISTICS, 78(1), February, 1996, 94-110 for a list of these

stylized facts in finance and a discussion of theories to explain them.

An attempt will be made to show what useful insights can be learned from

locating scaling laws and how to correct for improper treatment of

heterogeneity. In particular we will stress how "spurious" "unconditional"

scaling "laws" can easily be produced from a system of individual stochastic

processes relaxing to different stochastic steady states (even though the

relaxation rate is the same for each process). This exercise will stress the

importance of correctly controlling for heterogeneity, i.e. correctly

controlling for mixing observations from different distributions. Scaling

laws appear also in ecology and we will teach some of this material and draw

lessons from it for econometric practice.

VII. Adaptive Learning, Partially Identified Structures, Evolutionary

Learning

There has been much recent interest in econometrics in settings where

point identification is replaced by partial identification. See Charles

Manski's website at Northwestern for key articles and books on this topic as

well as a good entry point into this topic. Standard decision theory is being

modified to deal with decision making under data disciplined but partially

identified structures. This movement creates an interesting interaction

between econometrics and decision theory. It creates new opportunities to

evaluate decision making criteria on the basis of how fast decision makers

learn partially identified features of structures. A good place to start

reading this literature is Charles Manski's website at Northwestern and Larry

Epstein's website at Rochester.

More references are listed below.

(T. Sargent, (1993), BOUNDED RATIONALITY IN MACROECONOMICS, Oxford University

Press. Chen, X., White, H., (1994), "Nonparametric Adaptive Learning with

Feedback," UCSD Working Paper. Holland, J., (1992), ADAPTATION IN NATURAL AND

ARTIFICIAL SYSTEMS, MIT Press: Cambridge, MA. CeNDEF experiments on

expectation formation as well as other CeNDEF research on bounded rationality.

Fudenberg/Levine's book, THE THEORY OF LEARNING IN GAMES (1998), Larry

Samuelson's book on evolutionary games, Peyton Young's book on evolution of

conventions in games, ECONOPHYSICS website (see especially the links to

"minority games"), R. Selten's lab on strategy experiments in oligopoly

theory, CeNDEF work on strategy experiments in other types of games.)

The basic first year courses say little about dynamics and adaptive

learning towards a notion of "equilibrium." For example, Selten's lab at Bonn

has recently shown that optimization appears to play no role at all in

repeated oligopoly games (i.e. finite horizon supergames) with small numbers

of players. Rather something somewhat like Axelrod's TIT-FOR-TAT strategy

emerges as players evolve "ideal points" and induce play towards them by

"measure for measure".

First year courses say even less about any kind of socially interactive

learning on any kind of network or Selten-like behavior of players trying

to "train" each other towards a more cooperative outcome.

Since much of economics is based on equilibrium concepts which impose

restrictions on data which can be tested and since introduction of

"disequilibrium" concepts such as adaptive learning introduces extra "free

parameters," this imposes an even higher priority to discipline theorizing by

data than usual.

Researchers here, at the Santa Fe Institute, and other research centers

are trying to carry out this kind of research program consistent with observed

"scaling laws" and observed estimated conditional distributions in economics

and finance. We shall cover the basic methods and highlights of this new

literature. We shall also review experimental results. For example CeNDEF

has been using strategy experiments (originating from Selten's work) to

produce a set of stylized regularities about the expectations formation

process which is separated from other aspects of the game (such as strategy

involved via sharing a market as in oligopoly games) via a special design of

the experiment to "control-out" all other expects of the game except for the

expectation formation process itself.

Research on the "El Farol" problem (called the "minority game" by

physicists) has documented a "phase transition" and a "scaling law" (cf. work

on the ECONOPHYSICS website by Robert Savit of the University of Michigan and

many others). The parameters are "s" the "size of brain" of each player

(measured by the size of the strategy set available to each player), the "size

of the universal brain" (measured by the size of the universal set Omega(m) of

potential strategies that could be played) and memory "m" (measured by the

number of lagged observations allowed to be in each prediction function which

describes each forecasting strategy). The focus of the CeNDEF group is on the

dynamic evolution of adaptive forecasting systems whereas the focus of Savit

et al. is on uncovering "scaling" relationships and evidence of "phase

transitions" via computational experiments. There is also analytical work

reported on the ECONOPHYSICS website.

We shall spend some time comparing and contrasting these different

approaches to the modelling of adaptive learning as well as learning what we

can from results reported from laboratory experiments around the world. The

emphasis of this part of the course will be to develop model systems that

replicate experimental results, but at the same time develop analytical

methods for general use in this area.

Development of methods from natural science in searching for useful

"order parameters" to uncover "phase transitions" and "scaling laws" and

relating these to "scaling laws" from sampling theory in statistics (such as

central limit theorems, Edgeworth expansions, large deviations "scaling"

relations, breakdowns of central limit theorems due to series of cross

correlations diverging) will be stressed.

We will stress the incentive differences inherent in "small numbers"

adaptive (or other) "learning" situations of repeated play in contrast to

"large numbers" situations of repeated play. Selten's lab stressed the

inherent incentives of repeated "small numbers" play to "train" each other to

reach a cooperative outcome. Such incentives will get smaller as the number

of players increases because each player will be increasingly unable to

capture the benefits of her own "training efforts" onto the other players.

This relates to work on "evolution of norms and conventions" in Peyton Young's

book.

As one varies the number of players, the memory allowed in their

strategies, the size of their individual strategy sets and the size of the

size of all potential strategies of fixed memory as well as other quantifiable

aspects of the game the "order parameter" approach suggests looking for key

"order parameters" such that when an order parameter increases, the system

goes through an abrupt change in dynamical behavior (a "phase transition"

and/or a "bifurcation"). Analysis of continuous state space dynamical systems

of increasing size creates a demand for analytic results on eigenvalues of

dynamical systems of increasing size. Alan Edelman's website at MIT Math

contains very nice papers on this problem (e.g. "circular laws") which we

shall discuss.

Since we will be on new ground here, this should be an exciting part of

the course.

VIII. Cellular Automata, Ising Models, Spin Glass Models

Some references to this area are listed below.

(Mezard, M., Parisi, G., Virasoro, (1987), SPIN GLASS THEORY AND BEYOND, World

Scientific. Durlauf, S. (1993), REVIEW OF ECONOMIC STUDIES and various

working papers. Mitchell, M., Crutchfield, J., Hraber, P., (1994), "Evolving

Cellular Automata to Perform Computations: Mechanisms and Impediments,"

PHYSICA D, 75, 361-391. Doyon, B., Cessac, B., Quoy, M., Samuelides, M.,

(1993), "Control of the Transition to Chaos in Neural Networks with Random

Connectivity," INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, 3, #2,

279-291. Material on eigenvalues of large systems from Alan Edelman's website

at MIT Math. See material from Jim Crutchfield, Melanie Mitchell, and others

available by linking from the Santa Fe Institute's website.)

This material will give math modules from which we can build models of

adaptive interaction and parse out the components due to socially interactive

learning from "plain vanilla" adaptive expectations formation and other kinds

of "individualistic" adaptation. However, our posture will be somewhat

different than the large recent theory literature on networks and games. It

will be guided by a desire to formulate useful econometric frameworks where

tools like the Efficient Method of Moments (cf. George Tauchen's website at

Duke) and Computational Bayes (cf. John Geweke's website at Iowa, and his

paper, "Computational Experiments and Reality" available at his website at

University of Iowa, Iowa City, along with software available there) can be

used to measure the "statistical significance" of the "extra free parameters"

brought by adaptive learning theory. Emphasis will also be placed upon

econometrically separating interactive effects from empirically similar

looking effects due to correlated unobservables and other phenomena.

IX. Information Contagion, Polya Processes, Cascades, Self Reinforcing

Mechanisms, Magnification Mechanisms of Income and Wealth.

Some references are listed below.

(Arthur, W., (1988), "Self Reinforcing Mechanisms in Economics," in Arrow,

Anderson, Pines, op.cit. Bikhchandani, S., Hirshleifer, D., Welch, I.,

(1992), "A Theory of Fads, Fashion, Custom, and Cultural Change as

Informational Cascades," JOURNAL OF POLITICAL ECONOMY, 100 (5): 992-1026. De

Vany, A., and Walls, W., (1994), "Information, Adaptive Contracting, and

Distributional Dynamics: Bose-Einstein Statistics and the Movies," University

of California, Irvine, Working Paper, recently appeared in ECONOMIC JOURNAL.

Rosen, S., "The Economics of Superstars," AMERICAN ECONOMIC REVIEW, 71:

167-183.)

This material relates naturally to the above discussions in the sense

that it lays out a variety of channels through which interaction may operate

in a dynamically evolving social system as an economy. Emphasis will be

placed on econometric identification of the different "observable empirical

signatures" produced by each of these very different mechanisms of interaction

that may look the same to an econometric exercise if it is not carefully

formulated. Formulation of econometric exercises to differentiate different

channels of interaction including "social learning," "informational cascades,"

"positional reward structures" (e.g. "tournament" payoff structures), and

other related channels of possible interaction will take a very high priority

in this year's 606.

X. "Process vs. Equilibrium"

A common theme thoughout the above materials is moving thinking about the

economy away from "equilibrium" (even that of the stochastic process Real

Business Cycle type modelling) towards a view more like Artificial Life and

John Holland's Complex Adaptive Systems (cf. Leigh Tesfatsion's website, Tom

Ray's TIERRA, The Santa Fe Artificial Stock Market, "Sugarscape," and other

Artificial Life frameworks) where the system never settles down. This kind of

approach to economics can be viewed as a modern form of Austrianism. We shall

try to develop some analytics (rather like large system limits over a

hierarchy of "spatial" and temporal scales) to complement the exciting

computational work in this area.