Iterated Prisoner's Dilemma with Choice and Refusal of Partners

This article extends the traditional iterated prisoner's dilemma (IPD) with round-robin partner matching by permitting players to choose and refuse partners in each iteration on the basis of continually updated expected payoffs. Comparative computer experiments are reported that indicate the introduction of partner choice and refusal accelerates the emergence of mutual cooperation in the IPD relative to round-robin partner matching. Moreover, in contrast to findings for round-robin partner matching (in which the average payoffs of the players tend to be either clustered around the mutual cooperation payoff or widely scattered), the average payoff scores of the players with choice and refusal of partners tend to cluster into two or more distinct narrow bands. Preliminary analytical and computational sensitivity studies are also reported for several key parameters. Related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/tnghome.htmiterated prisoner's dilemma; preferential partner selection; evolutionary game theory

From Heterogeneous expectations to exchange rate dynamic:

The purpose of this paper is to analyze how heterogeneous behaviors of agents influence the exchange rates dynamic in the short and long terms. We examine how agents use the information and which kind of information, in order to take theirs decisions to form an expectation of the exchange rate. We investigate a methodology based on interactive agents simulations, following the Santa Fe Artificial Stock Market. Each trader is modeled as an autonomous, interactive agent and the aggregation of their behavior results in foreign exchange market dynamic. Genetic algorithm is the tool used to compute agents, and the simulated market tends to replicate the real EUR/USD exchange rate market. We consider six kinds of agents with pure behavior: fundamentalists, positive feedback traders and negative ones, naive traders, news traders (positive and negative). To reproduce stylized facts of the exchange rates dynamic, we conclude that the key factor is the correct proportion of each agents type, whiteout any need of mimetic behaviors, adaptive agents or pure noisy agentsexchange rates dynamic, heterogeneous interactive agents behaviour, genetic algorithm, learning process

Architecting system of systems: artificial life analysis of financial market behavior

This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3

Information Aggregation in Exponential Family Markets

We consider the design of prediction market mechanisms known as automated market makers. We show that we can design these mechanisms via the mold of \emph{exponential family distributions}, a popular and well-studied probability distribution template used in statistics. We give a full development of this relationship and explore a range of benefits. We draw connections between the information aggregation of market prices and the belief aggregation of learning agents that rely on exponential family distributions. We develop a very natural analysis of the market behavior as well as the price equilibrium under the assumption that the traders exhibit risk aversion according to exponential utility. We also consider similar aspects under alternative models, such as when traders are budget constrained

The virtues and vices of equilibrium and the future of financial economics

The use of equilibrium models in economics springs from the desire for parsimonious models of economic phenomena that take human reasoning into account. This approach has been the cornerstone of modern economic theory. We explain why this is so, extolling the virtues of equilibrium theory; then we present a critique and describe why this approach is inherently limited, and why economics needs to move in new directions if it is to continue to make progress. We stress that this shouldn't be a question of dogma, but should be resolved empirically. There are situations where equilibrium models provide useful predictions and there are situations where they can never provide useful predictions. There are also many situations where the jury is still out, i.e., where so far they fail to provide a good description of the world, but where proper extensions might change this. Our goal is to convince the skeptics that equilibrium models can be useful, but also to make traditional economists more aware of the limitations of equilibrium models. We sketch some alternative approaches and discuss why they should play an important role in future research in economics.Comment: 68 pages, one figur

A Grey-Box Approach to Automated Mechanism Design

Auctions play an important role in electronic commerce, and have been used to solve problems in distributed computing. Automated approaches to designing effective auction mechanisms are helpful in reducing the burden of traditional game theoretic, analytic approaches and in searching through the large space of possible auction mechanisms. This paper presents an approach to automated mechanism design (AMD) in the domain of double auctions. We describe a novel parametrized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.Comment: 18 pages, 2 figures, 2 tables, and 1 algorithm. Extended abstract to appear in the proceedings of AAMAS'201