On the Exploratory Function of Agent-Based Modeling

Agent-based models (ABM) derive the behavior of artificial socio-economic entities computationally from the actions of a large number of agents. One objection is that highly idealized ABMs fail to represent the real world in any reasonable sense. Another objection is that they at best show how observed patterns may have come about, because simulations are easy to produce and there is no evidence that this is really what happens. Moreover, different models may well yield the same result. I will rebut these objections by focusing on an often neglected, but crucial function of ABMs. Building on Gelfert’s (2016) account of the exploratory uses of scientific models I show that, in the absence of an accepted underlying theory, successful ABMs lend inductive support to assumptions concerning certain structural feutures of the behavioral rules employed. One core step towards this goal is what I call multiple-model robustness analysis.

Probability, interpretations of

The term ‘probability’ and its cognates occur frequently in both everyday and philosophical discourse. Unlike many other concepts, it is unprofitable to view ‘probability’ as having a unique meaning. Instead, there exist a number of distinct, albeit related, concepts, of which we here mention five: the classical or equiprobable view, the relative frequency view, the subjectivist or personalist view, the propensity view, and the logical probability view. None of these captures all of our legitimate uses of the term ‘probability’, which range from the clearly subjective, as in our assessment of the likelihood of one football team beating another, through the inferential, as when one set of sentences lends a degree of inductive support to another sentence, to the obviously objective, as in the physical chance of a radioactive atom decaying in the next minute. It is often said that what all these interpretations have in common is that they are all described by the same simple mathematical theory – ‘the theory of probability’ to be found in most elementary probability textbooks – and it has traditionally been the task of any interpretation to conform to that theory. But this saying does not hold up under closer examination, and it is better to consider each approach as dealing with a separate subject matter, the structure of which determines the structure of the appropriate calculus.

Denying the Antecedent: Its Effective Use in Argumentation

Denying the antecedent is an invalid form of reasoning that is typically identified and frowned upon as a formal fallacy. Contrary to arguments that it does not or at least should not occur, denying the antecedent is a legitimate and effective strategy for undermining a position. Since it is not a valid form of argument, it cannot prove that the position is false. But it can provide inductive evidence that this position is probably false. In this role, it is neither defective nor deceptive. Denying the antecedent provides inductive support for rejecting a claim as improbable.