Robert E. Marks

Breeding hybrid strategies: optimal behavior for oligopolists

Oligopolistic pricing decisions-in which the choice variable is not dichotomous as in the simple prisoners dilemma but continuous-have been modeled as a generalized prisoners dilemma (GPD) by Fader and Hauser, who sought, in the two MIT Computer Strategy Tournaments, to obtain an effective generalization of Rapoports Tit for Tat for the three-person repeated game. Hollands genetic algorithm and Axelrods representation of contingent strategies provide a means of generating new strategies in the computer, through machine learning, without outside submissions.The paper discusses how findings from two-person tournaments can be extended to the GPD, in particular how the authors winning strategy in the Second MIT Competitive Strategy Tournament could be bettered. The paper provides insight into how oligopolistic pricing competitors can successfully compete, and underlines the importance of “niche” strategies, successful against a particular environment of competitors.Bootstrapping, or breeding strategies against their peers, provides a means of examining whether “repetition leads to cooperation”: we show that it can, under certain conditions, for simple and extended two- and three-person GPD repeated games. The paper concludes with a discussion of the relationship between Seltens trembling-hand perfect equilibrium and Maynard Smiths evolutionarily stable strategies, with practical simulations of successful and unsuccessful “invasions” by new strategies.

Technical Efficiency, Technological Change and Total Factor Productivity Growth in Chinese State‐Owned Enterprises in the Early 1990s

Based on a framework proposed by Battese and Coelli (1995), we estimated the stochastic frontier production function for four Chinese industries: building materials, chemicals, machinery and textiles during 1990–1994. The type of technological change is tested and total factor productivity (TFP) growth is calculated for each industry. We found no evidence of technological change in the building materials, chemicals and textiles industries and a neutral technological progress in the machinery industry. There was significant reduction of technical efficiency in the chemicals, machinery and textiles industries. As the result, chemicals and textiles experienced a negative TFP growth, whereas building materials and machinery display negligible TFP change. Our result cast doubt on the industrial reform measures in China.

Genetic Algorithms In Economics and Finance: Forecasting Stock Market Prices And Foreign Exchange — A Review

After a brief overview of the history of the development and application of genetic algorithms and related simulation techniques, this chapter describes alternative implementations of the genetic algorithm, their strengths and weaknesses. Then follows an overview of published applications in finance, with particular focus on the papers of Bauer, Pereira, and Colin in foreign exchange trading. Many other rumored applications remain unpublished.

Evolved perception and behaviour in oligopolies

Abstract This paper builds on earlier studies which examined oligopolists in a repeated interaction as responding simply to past prices of their strategic rivals, and which used data from a mature market, with stable rules of thumb (mappings from past actions, or states of the market, to present prices) for the oligopolists’ behaviour, whether purposefully learnt or emerging from the natural selection of the rivalry. The earlier studies imposed exogenous partitions on the action space, as perceived by the players. This study explores how such perceptions might be endogenised. A firm answer to the question of how oligopolists partition their perceptions of others’ actions, both through time and across the price space, will also provide information on how much or how little information they choose to use: in short, how boundedly rational the oligopolists have chosen to be. We use data from a retail coffee market to examine the evolved optimal partitioning and mapping of price space, manifest as the oligopolists’ rules of thumb. The data suggest that brand managers are using very little information: whether prices changed or not.

Prohibition or Regulation: An Economist's View of Australian Heroin Policy

Concern for the spread of HIV infection and with the growing social costs associated with the policy of heroin prohibition have recently led many to reconsider the policy. A large question facing advocates of decriminalisation or legalisation is to what extent the numbers of users would grow under a more regulated scheme. “More regulated” because under prohibition there is a completely unregulated market, which is, however, illegal: lawless laissez-faire. This article advocates some degree of regulation for the supply of heroin, and abandonment of the unsuccessful policy of prohibition. In attempting to answer the question of the number of users under different regimes — and their importance to society — the article closely examines the structure of the black market, using a previously unpublished survey of the illegal industry performed in Victoria some years ago by the illicit industry itself. This confirms recent findings that there are relatively large numbers of occasional users who seldom come to the attention of medical or law-enforcement authorities, and whose heroin use per se imposes little cost on society.

A Freer Market for Heroin in Australia: Alternatives to Subsidizing Organized Crime

The problems associated with illicit drug use in general, and the illicit use of heroin in particular, have led to stringent attempts by Australian governments to enforce the laws against drug abuse. The strongest reaction of the criminal justice system has been toward heroin, with a total prohibition on heroin importation, manufacture, distribution, possession, and use. Before attempting to evaluate the extent and costs of heroin use today, this paper reviews the evolution of laws and social attitudes toward heroin in Australia. Using an economic framework for analyzing the black market in heroin, the paper examines proposals for enforcing the prohibition by tightening the supply side, and by reducing the demand for heroin. It argues that attempts to restrict the supply have had the effect of increasing the costs borne not only by the users but by society at large, through increases in acquisitive crime and police corruption. On utilitarian grounds it concludes that the costs to society of the prohibition far outweigh the costs of a policy of freer availability, and suggests that a policy of government supply of price-controlled heroin and methadone would be far preferable to todays failed policy of prohibition.

Adaptive Behaviour in an Oligopoly

Advances in game theory have provided an impetus for renewed investigation of the strategic behaviour of oligopolists as players in repeated games. Marketing databases provide a rich source of historical evidence of such behaviour. This paper uses such data to examine how players in iterated oligopolies respond to their rivals’ behaviour, and uses machine learning to derive improved contingent strategies for such markets, in order to provide insights into the evolution of such markets and the patterns of behaviour observed. The paper is an application of repeated games and machine learning to adaptive behaviour over time in the retail market for ground coffee. Using empirical data on the weekly prices and promotional instruments of the four largest and several smaller coffee sellers in a regional U.S. retail market, and using a market model to predict sellers’ market shares and profits in response to others’ actions in any week, we examine the adaptive strategic behaviour of the three largest sellers. We model the sellers’ strategic behaviours as finite automata with memory of previous weeks’ actions, and use the Axelrod/Forrest representation of the action function, mapping state to action. We use a genetic algorithm (GA) to derive automata which are fit, given their environment, as described by their rivals’ actions in the past and the implicit demand for coffee.

Using genetic algorithms to breed competitive marketing strategies

Genetic algorithms (GAs) have been used for mappings which optimize a repeated procedure. An offshoot of this has been their use in what has been called co-evolution of mappings. This paper reports results from a project in which GAs have been used to: 1) to derive mappings which may explain the behavior of brand managers in an oligopolistic retail market for coffee; and 2) to attempt to improve on the historical profits of these brand managers, pitted in weekly competition with each other, vying for sales and profits with their different brands of ground, sealed coffee on the supermarket shelves. As well as advancing the practice of GAs, with separate populations competing, the work also advances our understanding of modeling players in repeated oligopolistic interactions or games.

Validation and model selection: Three similarity measures compared

There are two types of simulation models: Demonstration models, essentially existence proofs for phenomena of interest, and Descriptive models, that attempt to track dynamic historical phenomena. Both types require verification. Descriptive models require validation against historical data as well. More broadly, we can think of a process of choosing the “best” of several models. This paper examines three measures of the similarity of two sets of vectors, here time series. The best known but flawed is the Kullback-Leibler information-theoretic construct. A second measure is what I have called the State Similarity Measure. The third measure is a set-theoretic measure of similarity, the Generalized Hartley Metric. For illustration, we use data from a dynamic simulation model of historical brand rivalry.

Playing Games with Genetic Algorithms

In 1987 the first published research appeared which used the Genetic Algorithm as a means of seeking better strategies in playing the repeated Prisoner’s Dilemma. Since then the application of Genetic Algorithms to game-theoretical models has been used in many ways. To seek better strategies in historical oligopolistic interactions, to model economic learning, and to explore the support of cooperation in repeated interactions. This brief survey summarises related work and publications over the past thirteen years. It includes discussions of the use of game-playing automata, co-evolution of strategies, adaptive learning, a comparison of evolutionary game theory and the Genetic Algorithm, the incorporation of historical data into evolutionary simulations, and the problems of economic simulations using real-world data.