Diversity and Democracy: Agent-Based Modeling in Political Philosophy

Agent-based models have played a prominent role in recent debates about the merits of democracy. In particular, the formal model of Lu Hong and Scott Page and the associated “diversity trumps ability” result has typically been seen to support the epistemic virtues of democracy over epistocracy (i.e., governance by experts). In this paper we first identify the modeling choices embodied in the original formal model and then critique the application of the Hong-Page results to philosophical debates on the relative merits of democracy. In particular we argue that the “best-performing agents” in Hong-Page model should not be interpreted as experts. We next explore a closely related model in which best-performing agents are more plausibly seen as experts and show that the diversity trumps ability result fails to hold. However, with changes in other parameters (such as the deliberation dynamic) the diversity trumps ability result is restored. The sensitivity of this result to parameter choices illustrates the complexity of the link between formal modeling and more general philosophical claims; we use this debate as a platform for a more general discussion of when and how agent-based models can contribute to philosophical discussions. Key-wordsEpistemic democracy · Epistocracy · Expertise · Agent-based Models · Representation · Deliberation · Cognitive diversity · Problem solving · Democracy · Wise crowds Author Affiliations: Bennett Holman: Assistant Professor of History and Philosophy of Science, Underwood International College, Yonsei University William J. Berger: Post-Doctoral Fellow, University of Pennsylvania Daniel J. Singer: Assistant Professor, University of Pennsylvania Patrick Grim: Professor Emeritus, State University of New York at Stony Brook; Philosopher in Residence, Center for Complex Systems, University of Michigan Aaron Bramson: Research Scientist, Riken Brain Science Institute; Affiliate Researcher, Department of General Economics, Gent University & Adjunct Professor, Department of Software and Information Systems, UNC Charlotte, The view that it is more proper for the multitude to be sovereign than the few of greatest virtue might be thought to be explicable and to have some justification, and even to be the true view. For it is possible that the many, though not individually good men, yet when they come together may be better, not individually but collectively, than those who are so, just as public dinners to which many contribute are better than those supplied at one man's cost; for where there are many, each individual, it may be argued, has some portion of virtue and wisdom, and when they have come together, just as the multitude becomes a single man with many feet and many hands and many senses, so also it becomes one personality as regards the moral and intellectual faculties. (Aristotle Pol 3.1281a-b) A recognition of the ‘wisdom of the crowds’ has been a source of optimism for democratic theorists, suggesting democracy has a competitive advantage over alternative regimes. Some political philosophers have made agent-based modeling (ABM) central to this discussion with their claim that Lu Hong and Scott Page’s (2004) “diversity trumps ability” result (DTA) formally indicates the epistemic virtues of democracy (Anderson, 2006; Gaus, 2016; Landemore, 2013a;b; Schwartzberg, 2015). But there has also been pushback to this move, questioning whether the Hong-Page results have such an implication (Ahlstrom-Vij, 2012; Weymark, 2015). Some have argued that the Hong-Page results are compatible with (or positively support) a form of epistocracy, where the knowers or experts dictate the policy of the state (Ancell, 2016; Brennan, 2016). Here we attempt to go deeper into the models crucial to the debate, but also attempt to go beyond those specifics in order to reflect more generally on the promise and pitfalls of attempts to draw philosophical lessons from agent-based models. We present our own extensions to the Hong-Page model, illustrating with that example how such an application of ABMs can make philosophical argumentation more rigorous. In section one, we discuss the Hong-Page results, describing both the dynamics of the ABM and the modeling decisions therein. In section II, we argue that interpreting the Hong-Page results as pertaining to predictive expertise is implausible. We identify test-retest reliability as a minimal criterion of predictive expertise and show that the “best-performing agents” in the HongPage model fail to meet such a criterion. In section III we explore a set of extensions to the original model, showing that the Hong-Page ‘Diversity Trumps Ability’ result fails to hold in parameter subspaces in which claims of predictive expertise are more plausible. In section IV we explore the parameter sensitivity of this result and show that the DTA result reemerges with other suites of parameters. The remainder of the paper pivots from formal models to the wider question of philosophical interpretation and social application. Section V provides an illustration of how such models are used in political philosophy, and how extensions such as ours might reflect on the limited generality of such arguments. We use Helene Landemore’s Democratic Reason (2013a) as a case study due to its influential application of ABMs to questions in political philosophy. In that regard, we show how our extensions offer further refinements to the conjectures presented in her work. Finally, section VI argues for the importance of carefully identifying modeling assumptions and relevant extensions, particularly when leveraging ABMs in practical literatures such as political philosophy. We conclude by suggesting what further steps are needed to bridge the gap between the two. I. Unpacking the Hong Page result The Hong-Page model consists of three important components: (1) Problems and problem characteristics (epistemic landscapes); (2) Agents; (3) Individual problem solving; (4) Group formation; and (5) Group problem solving dynamics. In the model, sets of agents navigate an epistemic landscape in an attempt to identify the landscape’s highest point (the global maximum). In this section, we examine the model in two passes. First, we explain these five features of the Hong-Page model dynamics and we explore the modeling choices made in each case. We will return to discuss the importance of these modeling choices later in the paper.