Submissions to the Special Issue of KER


THE EDITORS of the Cambridge University Press journal, The Knowledge Engineering Review, invited Robert Marks (AGSM, University of New South Wales) and Nick Vriend (Queen Mary University, London) to edit a special issue of KER devoted to agent- based or multi-agent simulation models in economics.

http://journals.cambridge.org/action/displayJournal?jid=KER

The focus of KER is on review and survey papers in the general field of artificial intelligence, and the clarification and dissemination of its methods and concepts. Accepted papers in the special issue will provide balaced but critical presentations of the primary concepts in the area of ACE/MAS in economics. Submissions underwent a peer-review process of revision and acceptance/rejection.

Agent-based computational economics (ACE) or multi-agent simulation (MAS) models in economics seek to use computationally intensive bottom-up techniques to study economic processes of interacting agents. Such studies are less likely to stress the tradional trio of the existence, uniqueness, and stability of equilibria and more likely to focus on economic processes, local interactions, and out-of-equilibria dynamics, to quote the Preface to the Handbook of Agent-Based Computational Economics, edited by Tesfatsion & Judd (2006), the definitive statement of ACE.

As guest editors, we sought articles that survey developments of ACE/MAS in economics and finance in the past two or three years, or that cover areas (such as model assurance -- verification and validation) that were not covered in the 2006 anthology.

The Special Issue will appear in two parts in early 2012 (Volume 27, Issue 1, et seq.). The contents are:

Special Issue, Part 1:

  1. Robert Marks

    Analysis and Synthesis: Multi-Agent Systems in the Social Sciences

    Abstract: Although they flow from a common source, the uses of multi-agent systems (or "agent-based computational systems" -- ACE) vary between the social sciences and computer science. The distinction can be broadly summarised as analysis versus synthesis, or explanation versus design. I compare and contrast these uses, and discuss sufficiency and necessity in simulations in general and in multi-agent systems in particular, with a computer science audience in mind.
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  2. Matteo Richiardi

    Agent-based Computational Economics. A Short Introduction.

    Abstract: In a nutshell, agent-based models (ABM) are models, i.e. abstract representation of the reality, in which (i) a multitude of objects interact with each other and with the environment, (ii) the objects are autonomous, i.e. there is no central, or "top down" control over their behavior 1, and (iii) the outcome of their interaction is numerically computed. Since the objects are autonomous, they are called "agents". As Leigh Tesfatsion -- one the leading researchers in the field and the "mother" of the ACE acronym, which describes the application of ABM to Economics -- defines it, Agent-based Computational Economics (ACE) is: "the computational study of economic processes modeled as dynamic systems of interacting agents."
    Note that none of the two features above, in isolation, defines the methodology: the micro-perspective implied by (i) and (ii) is the same adopted, for instance, by game theory, where strategic interaction is investigated analytically, while the computational approach is typical of Computational General Equilibrium or System Dynamics, which however are based on aggregate representations of the system.
    In this paper we will describe in more details the features of ABM (section 1), offer an overview of their historical development (section 2), discuss when they can be fruitfully employed (section 2.3), and how they can be combined with more traditional approaches.
    While maintaining a "low profile" in describing the approach, we will offer a strong defense of its methodological soundness (section 3). In particular, we will argue that (i) ABM are mathematical models, (ii) ABM may lead -- as analytical models -- to general results, and (iii) ABM can be taken to the data, i.e. estimated empirically. However, in this survey paper we will not discuss the issue of validation of agent-based models, first of all because many problems in validation are the same encountered with more traditional (analytic) models. The interested reader is referred to the recent (2007) special number of Computational Economics explicitly devoted to empirical validation of agent- based models, and in particular to [Fagiolo et al., 2007, Marks, 2007].
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  3. Scott Page

    Aggregation in Agent-Based Models of Economics

    Abstract: Agent-based models are often described as bottom-up. Macro-level phenomena emerge from the micro-level interactions of agents. These macro-level phenomena include fixed points, dynamic patterns, and long transients. In this paper, I explore the link between micro-level characteristics -- learning rules, diversity, network structure, and externalities -- and the macro-level patterns they produce. I focus on why we need agent-level modeling, on how these models produce emergent phenomenon, and on how agent-based models help understand outcomes of social systems in a way that differs from the analytic, equilibrium approach.
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  4. Giorgio Fagiolo, Andrea Roventini,

    On the Scientific Status of Economic Policy: A Tale of Alternative Paradigms

    Abstract: In the last years, a number of contributions have argued that monetary -- and, more generally, economic -- policy is finally becoming more of a science. According to these authors, policy rules implemented by central banks are nowadays well supported by a theoretical framework (the New Neoclassical Synthesis) upon which a general consensus has emerged in the economic profession. In other words, scientific discussion on economic policy seems to be ultimately confined to either fine-tuning this "consensus" model, or assessing the extent to which elements of art still exist in the conduct of monetary policy. In this paper, we present a substantially opposite view, rooted in a critical discussion of the theoretical, empirical and political-economy pitfalls of the neoclassical approach to policy analysis. Our discussion indicates that we are still far from building a science of economic policy. We suggest that a more fruitful research avenue to pursue is to explore alternative theoretical paradigms, which can escape the strong theoretical requirements of neoclassical models (e.g., equilibrium, rationality, etc.). We briefly introduce one of the most successful alternative research projects -- known in the literature as agent-based computational economics (ACE) -- and we present the way it has been applied to policy analysis issues. We conclude by discussing the methodological status of ACE, as well as the (many) problems it raises.
    Keywords: Economic Policy, Monetary Policy, New Neoclassical Synthesis, New Keynesian Models, DSGE Models, Agent-Based Computational Economics, Agent-Based Models.
    JEL Classification: B41, B50, E32, E52.
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  5. Al Wilhite and Eric A. Fong:

    Agent-Based Models and Hypothesis Testing: An Example of Innovation and Organizational Structure

    Abstract: Hypothesis testing is uncommon in agent-based modeling and there is an array of reasons as to why (see Fagiolo, Windrum, and Moneta (2006) for a review). This is one of those uncommon studies; a combination of the new and old. First, a traditional neo-classical model of decision making is broadened by introducing agents who interact in an organization. The resulting computational model is analyzed using virtual experiments to consider how different organizational structures (different network topologies) affect the evolutionary path of an organization's corporate culture. These computational experiments establish testable hypotheses concerning structure, culture, and performance, and those hypotheses are tested empirically using data from an international sample of firms. In addition to learning something about organizational structure and innovation, the paper demonstrates how computational models can be used to frame empirical investigations and facilitate the interpretation of results in a traditional fashion.
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Special Issue, Part 2

  1. Shu-Heng Chen, Chia-Ling Chang, Yeh-Jung Du,

    Agent-Based Economic Models and Econometrics

    Abstract: This paper reviews the development of agent-based (computational) economics (ACE) from an econometrics viewpoint. The review comprises of three stages, characterizing the past, the present, and the future of this development. The first two stages can be interpreted as an attempt to build the econometric foundation of ACE, and, through that, enrich its empirical content. The second stage may then invoke a reverse reflection on the possible agent-based foundation of econometrics. While ACE modeling has been applied to different branches of economics, the one, and probably the only one, that is able to evidence this three-stage development is finance or financial economics. We, therefore, will focus our review only on the literature of agent-based computational finance, or more specifically, agent-based modeling of financial markets.
    Keywords: Agent-Based Economics, N-Type Designs, Autonomous-Agent Designs, Econometrics, Simulation-Based Econometrics, Market Fraction Hypothesis
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  2. Jasmina Arifovic, John Ledyard,

    Individual Evolutionary Learning with Many Agents

    Abstract: Individual Evolutionary Learning (IEL) is a learning model based on the evolution of a population of strategies of an individual agent. In prior work, IEL has been shown to be consistent with the behavior of human subjects in games with a small number of agents. In this paper, we examine the performance of IEL in games with many agents. We find IEL to be robust to this type of scaling. With the appropriate linear adjustment of the mechanism parameter, the convergence behavior of IEL in games induced by Groves-Ledyard mechanisms in quadratic environments is independent of the number of participants.
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  3. Mikhail Anufriev, Cars Hommes,

    Evolution of Market Heuristics,

    Abstract: The time evolution of aggregate economic variables, such as stock prices, is affected by market expectations of individual investors. Neo-classical economic theory assumes that individuals form expectations rationally, thus enforcing prices to track economic fundamentals and leading to an efficient allocation of resources. However, laboratory experiments with human subjects have shown that individuals do not behave fully rationally but instead follow simple heuristics. In laboratory markets prices may show persistent deviations from fundamentals similar to the large swings observed in real stock prices. Here we show that evolutionary selection among simple forecasting heuristics can explain coordination of individual behavior, leading to three different aggregate outcomes observed in recent laboratory market forecasting experiments: slow monotonic price convergence, oscillatory dampened price fluctuations and persistent price oscillations. In our model forecasting strategies are selected every period from a small population of plausible heuristics, such as adaptive expectations and trend following rules. Individuals adapt their strategies over time, based on the relative forecasting performance of the heuristics. As a result, the evolutionary switching mechanism exhibits path dependence and matches individual forecasting behavior as well as aggregate market outcomes in the experiments. Our results are in line with recent work on agent-based models of interaction and contribute to a behavioral explanation of universal features of financial markets.
    JEL codes: E37, G12, D84, C91, C92.
    Keywords: Expectations feedback; Experiments; Heuristics; Evolutionary learning; Asset pricing model.
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  4. Dan Ladley

    Zero Intelligence in Economics and Finance

    Abstract: This paper reviews the Zero Intelligence methodology for investigating markets. This approach models individual traders, operating within a market mechanism, who behave without strategy, in order to determine the impact of the market mechanism and consequently the effect of trader behaviour. The paper considers the major contributions and models within this area from both the economics and finance communities before going on to examine the strengths and weaknesses of this methodology.
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