Please email me to see a draft (arnon.levy[that symbol]mail.huji.ac.il)
“Bringing Back Thought Experiments into The Philosophy of Science.” (W/ Adrian Currie, under review. )
To a large extent, the evidential base of claims in the philosophy of science has switched from thought experiments to case studies. We argue that this is a wrong turn: thought experiments and case studies can work complementarily. We make our argument via an analogy with the relationship between experiments and observations within science. Just as experiments and ‘natural’ observations can together evidence claims in science, each mitigating the downsides of the other, so too can thought experiments and case studies be mutually supporting. After presenting the main argument, we look at potential concerns about thought experiments, suggesting that a judiciously applied mixed-methods approach can overcome them.
To a large extent, the evidential base of claims in the philosophy of science has switched from thought experiments to case studies. We argue that this is a wrong turn: thought experiments and case studies can work complementarily. We make our argument via an analogy with the relationship between experiments and observations within science. Just as experiments and ‘natural’ observations can together evidence claims in science, each mitigating the downsides of the other, so too can thought experiments and case studies be mutually supporting. After presenting the main argument, we look at potential concerns about thought experiments, suggesting that a judiciously applied mixed-methods approach can overcome them.
“Inductive Risk and Value-Freedom: Jeffrey Revisited”
The paper aims to buck the recent trend of rejecting the value-freedom of science. I do so in part by revisiting and recasting Jeffrey’s (1956) response to Rudner’s (1953) and Douglas’ (2000, 2009) Argument from Inductive Risk (AIR). I divide Jeffrey’s contribution into two parts – a critique of Rudner, and a positive proposal. I show that, contrary to what is usually assumed, Jeffrey’s view is not closely tied to probabilism and that it can be grounded in a systematic fashion by appeal to consequentialism. I then go on to develop the positive part of Jeffrey’s view into a division-of-labor rationale for separating the roles of scientists and policy professionals. I conclude by considering several 21st Century developments of the AIR, suggesting that they raise important, but not insurmountable challenges to the view developed earlier in the paper.
The paper aims to buck the recent trend of rejecting the value-freedom of science. I do so in part by revisiting and recasting Jeffrey’s (1956) response to Rudner’s (1953) and Douglas’ (2000, 2009) Argument from Inductive Risk (AIR). I divide Jeffrey’s contribution into two parts – a critique of Rudner, and a positive proposal. I show that, contrary to what is usually assumed, Jeffrey’s view is not closely tied to probabilism and that it can be grounded in a systematic fashion by appeal to consequentialism. I then go on to develop the positive part of Jeffrey’s view into a division-of-labor rationale for separating the roles of scientists and policy professionals. I conclude by considering several 21st Century developments of the AIR, suggesting that they raise important, but not insurmountable challenges to the view developed earlier in the paper.
“Approximating Bayes? On the Notion of Approximation in Bayesian Cognitive Science” (Under Review)
Approximations have come to occupy a central role within Bayesian cognitive science. Many cognitive scientists view current approximation-based models as showing that the mind approximates Bayesian inference. In this paper I probe this idea, asking what it means to claim that the mind approximated one computation by executing another. I look at three possible interpretations of such claims, finding problems with each. I argue that this poses challenges to one central rationale for the Bayesian approach, and to its top-down methodology.
Approximations have come to occupy a central role within Bayesian cognitive science. Many cognitive scientists view current approximation-based models as showing that the mind approximates Bayesian inference. In this paper I probe this idea, asking what it means to claim that the mind approximated one computation by executing another. I look at three possible interpretations of such claims, finding problems with each. I argue that this poses challenges to one central rationale for the Bayesian approach, and to its top-down methodology.
“What’s So Good about Being Optimal? On the Optimality Approach in Cognitive Science” (W/ Lotem Elber-Dorozko).
We look at the role of optimality assumptions in cognitive science: what is their nature? Hoe do they function? And, especially, how are the justified? Our discussion draws on philosophy of biology and on general philosophy of science in an attempt to clarify the pros and cons, and the potential pitfalls associated with optimality modeling of cognition.. We lay out three rationales for optimality modeling: methodological, empirical and interpretive. While we think each has its place, we warn especially against confusing them and moving between them illicitly.
We look at the role of optimality assumptions in cognitive science: what is their nature? Hoe do they function? And, especially, how are the justified? Our discussion draws on philosophy of biology and on general philosophy of science in an attempt to clarify the pros and cons, and the potential pitfalls associated with optimality modeling of cognition.. We lay out three rationales for optimality modeling: methodological, empirical and interpretive. While we think each has its place, we warn especially against confusing them and moving between them illicitly.
“Questions, Answers and Explanations” (W/ William Bechtel)
Abstract coming soon
Abstract coming soon
