Student Papers, Complex Adaptive Systems Class, Fall 2011

Review of Evolutionary Political Economy

We are entering the third decade of the twenty-first century with profound uncertainties and crucial challenges for the world economy. Phenomena like climate change, digital transformation, migration, demographic changes, and the ongoing COVID pandemic need to be understood and promptly addressed. We argue that the agent-based approach in economics is well suited to tackle these topics, because of its capacity to integrate the “micro” and “macro” dimensions by modelling the network of interactions among heterogeneous economic agents and their aggregate outcomes. This paper explains why the agent-based methodology is needed to overcome the limitations of the neoclassical approach in economics, which has not been able to properly address those challenges. To do so, the paper retraces the main stages of the scientific evolution in a general historical and epistemological perspective, showing how the paradigm of reductionism, which led to extraordinary advances after the scientific revo...

Eastern Economic Journal, 2008

LeBaron: Agent-based economics, and more generally agent-based social sciences, have been around in various forms for over 30 years. The advent of higher speed computing and new tools for the computational learning fields led to a major increase in activity in the early 1990s through today. Research activity continues to increase at the current time, but the field still remains somewhat of a ''niche field'' inside economics. Certain conferences and certain regions (such as Europe) are well populated with agent-based activity. However, at mainstream conferences inside the US one would have a hard time in finding agent-based researchers.

In this paper we explain some ideas about the role of agent based modelling in economics. We suggest that more realistic economic models and theories can be built under a framework which includes evolutionary thinking, experimental economics and psychology. In this context, agent based modelling can help us to translate some of the economic verbal accounting underlying those disciplines into real economic models. Moreover, experimental economics and agent based modelling jointly are the "laboratory" of the economic science. We explain the limitations of neoclassical economics in several economic disciplines, like finance, industry dynamics, economic geography, etc, and we show how agent based modelling can overcome some of these limitations.

2003

In this paper I review the main strengths and weaknesses of agent-based computational models. In particular I rationalise the main theoretical critiques, which point to the following problematic areas: (i) interpretation of the simulation dynamics, (ii) estimation of the simulation model, and (iii) generalisation of the results. I show that there exist solutions for all these issues. Moreover, this paper clarifies some confounding differences in terminology between the computer science and the economic literature.

2010

This article discusses some issues and challenges facing modern macroeconomics. We argue for the necessity to replace the reductionist approach at the heart of mainstream DSGE models with an approach rooted on the science of complexity and agent-based modelling. To strengthen and exemplify our position, we show a simple example and introduce several items for a research agenda along these lines. We would like to thank two anonymous referees, the Editor-in-Chief, Emiliano Santoro and Roberto Tamborini for helpful suggestions and comments on an earlier version. Saul Desiderio provided excellent research assistance.

2006

This paper addresses the problem of finding the appropriate method for conducting empirical validation in agent-based (AB) models, which is often regarded as the Achilles' heel of the AB approach to economic modelling. The paper has two objectives. First, to identify key issues facing AB economists engaged in empirical validation. Second, to critically appraise the extent to which alternative approaches deal with these issues. We identify a first set of issues that are common to both AB and neoclassical modellers and a second set of issues which are specific to AB modellers. This second set of issues is captured in a novel taxonomy, which takes into consideration the nature of the object under study, the goal of the analysis, the nature of the modelling assumptions, and the methodology of the analysis. Having identified the nature and causes of heterogeneity in empirical validation, we examine three important approaches to validation that have been developed in AB economics: indirect calibration, the Werker-Brenner approach, and the history-friendly approach. We also discuss a set of open questions within empirical validation. These include the trade-off between empirical support and tractability of findings, the issue of over-parameterisation, unconditional objects, counterfactuals, and the non-neutrality of data.

2007

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This chapter discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.

In this paper I review the main strengths and weaknesses of agent-based computational models. In particular I rationalise the main theoretical critiques, which point to the following problematic areas: (i) interpretation of the simulation dynamics, (ii) estimation of the simulation model, and (iii) generalisation of the results. I show that there exist solutions for all these issues. Moreover, this paper clarifies some confounding differences in terminology between the computer science and the economic literature.

The Knowledge Engineering Review, 2012

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. 2 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 chapter 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.

Agent-Based Social Systems, 2017