The backward induction controversy in game theory flared up and then practically ended within a decade – the 1990s. The protagonists, however, did not converge on an agreement about the source of the controversy. Why was this the case, if opposing sides had access to the same modelling techniques and empirical facts? In this paper I offer an explanation for this controversy and its unsettled end. The answer is not to be found in the modelling claims made by the opposing (...) protagonists, but in the tacit metaphors they operate under. Aristotle defined metaphor as giving a 'thing a name that belongs to something else' (Poetica, 1457b). The meaning of metaphors has not changed much since then – in contrast to models which are comparatively new, and still not well-understood, scientific tools. The controversy of backward induction in game theory provides a test bed for the explanatory power of metaphors. This paper frames the controversy in terms of metaphor choice to provide a common framework for the protagonists. This results in the identification of three different domains – mathematical logic, game theory and the world – each connected to the other via different metaphors. The controversy around backward induction is placed in, and tentatively explained by, this framework. (shrink)

Theoretical works in economics usually have a core consisting of proofs that a “model-economy” has certain properties. The economist constructs a model that can be looked on as a description of an economy, and then proves that certain relations hold in this economy and/or that certain relations in this economy depend on certain specific characteristics. The model-economy is usually described as simplified or idealized.

What Daniel Hausman has called “the simple criticism of economic theory”1 is succinctly conveyed by the following passage: “We know full well not only that commodities are not infinitely divisible (which is only intended as a simplification), but businessmen do not always attempt to maximize profits and that the preferences of consumers are not always transitive. ‘Businessmen maximize profits’ and ‘a consumer’s preferences are transitive’ are fundamental economic ‘laws’. How can economists rationally accept a theory which is so full of (...) falsehoods?” (1981a, p. 382). In a recent paper Hausman considers several defenses of neoclassical microeconomics against the simple criticism. One of them is the modal view which Hausman initially characterizes in the, following admittedly vague and ambiguous manner: “Microeconomic general statements are modal (counter-factual) claims. (shrink)

Policy-makers face an uncertain world. One way of getting a handle on decision-making in such an environment is to rely on evidence. Despite the recent increase in post-fact figures in politics, evidence-based policymaking takes centre stage in policy-setting institutions. Often, however, policy-makers face large volumes of evidence from different sources. Robustness analysis can, prima facie, handle this evidential diversity. Roughly, a hypothesis is supported by robust evidence if the different evidential sources are in agreement. In this thesis, I strengthen the (...) case for the use of robustness analysis in evidence-based policymaking by answering open research questions about this inference technique. First, I argue that existing taxonomies miss a fruitful category of robustness reasoning, that is predictive stability. Second, I claim that derivational robustness analysis – the investigation of whether the results of different models are in agreement – can yield interesting insights even if not the entire relevant model space is covered by available models or if the model results are only partially in agreement. Third, I claim that expert knowledge is necessary to address questions that arise when one applies measurement robustness analysis – the investigation into whether multiple means of measurement yield the same result. Finally, I argue that, in situations where evidence from different measurements is not in agreement, it can be advisable to no longer take all of the evidence into account. This can be done in a rationally defensible way by choosing the most adequate theory or model underlying parts of the evidence set. I discuss examples from climate, medical, and economic policy-making to establish my claims. (shrink)

How is it possible that models from game theory, which are typically highly idealised, can be harnessed for designing institutions through which we interact? I argue that game theory assumes that social interactions have a specific structure, which is uncovered with the help of directed graphs. The graphs make explicit how game theory encodes counterfactual information in natural collections of its models and can therefore be used to track how model-interventions change model-outcomes. For model-interventions to inform real-world design requires the (...) truth of a causal hypothesis, namely that structural relations specified in a model approximate causal relations in the target interaction; or in other words, that the directed graph can be interpreted causally. In order to increase their confidence in this hypothesis, market designers complement their models with natural and laboratory experiments, and computational methods. Throughout the paper, the reform of a matching market for medical residents provides a case study for my proposed view, which hasn't been previously considered in the philosophy of science. (shrink)