When John Harsanyi came to Stanford University as a candidate for the Ph.D., I asked him why he was bothering, since it was most un likely that he had anything to learn from us. He was already a known scho lar; in addition to some papers in economics, the first two papers in this vol ume had already been published and had dazzled me by their originality and their combination of philosophical insight and technical competence. However, I am very glad (...) I did not discourage him; whether he learned any thing worthwhile I don't know, but we all learned much from him on the foundations of the theory of games and specifically on the outcome of bar gaining. The central focus of Harsanyi's work has continued to be in the theory of games, but especially on the foundations and conceptual problems. The theory of games, properly understood, is a very broad approach to social interaction based on individually rational behavior, and it connects closely with fundamental methodological and substantive issues in social science and in ethics. An indication of the range of Harsanyi's interest in game the ory can be found in the first paper of Part B -though in fact his owncontri butions are much broader-and in the second paper the applications to the methodology of social science. The remaining papers in that section show more specifically the richness of game theory in specific applications. (shrink)

Loop analysis is a method of qualitative modeling anticipated by Sewall Wright and systematically developed by Richard Levins. In Levins’ (1966) distinctions between modeling strategies, loop analysis sacrifices precision for generality and realism. Besides criticizing the clarity of these distinctions, Orzack and Sober (1993) argued qualitative modeling is conceptually and methodologically problematic. Loop analysis of the stability of ecological communities shows this criticism is unjustified. It presupposes an overly narrow view of qualitative modeling and underestimates the broad role models play (...) in scientific research, especially in helping scientists represent and understand complex systems. (shrink)

Ecologists have proposed several incompatible definitions of ecological stability. Emulating physicists, mathematical ecologists commonly define it as Lyapunov stability. This formalizes the problematic concept by integrating it into a well‐developed mathematical theory. The formalization also seems to capture the intuition that ecological stability depends on how ecological systems respond to perturbation. Despite these advantages, this definition is flawed. Although Lyapunov stability adequately characterizes perturbation responses of many systems studied in physics, it does not for ecological systems. This failure reveals a (...) limitation of its underlying mathematical theory, and an important difference between dynamic systems modeling in physics and ecology. *Received March 2006; revised June 2008. †To contact the author, please write to: Philosophy Department, 151 Dodd Hall, Florida State University, Tallahassee, FL 32306; e‐mail: [email protected]. (shrink)

This paper summarizes and rebuts the three standard objections made by social choice theorists against interpersonal utility. The first objection argues that interpersonal utility is measningless. I show that this objection either focuses on irrelevant kinds of meaning or else uses implausible criteria of meaningfulness. The second objection argues that interpersonal utility has no role to play in social choice theory. I show that on the contrary interpersonal utility is useful in formulating goals for social choice. The third objection argues (...) that interpersonal utility in social choice theory can be replaced by clearer notions. I show that the replacements proposed are unsatisfactory in either interpersonal utility's descriptive or explanatory role. My conclusion is that interpersonal utility has a legitimate place in social choice theory. (shrink)

Roald Hoffmann and other theorists claim that we ought to use highly idealized chemical models (“qualitative models”) in order to increase our understanding of chemical phenomena, even though other models are available which make more highly accurate predictions. I assess this norm by examining one of the tradeoffs faced by model builders and model users—the tradeoff between precision and generality. After arguing that this tradeoff obtains in many cases, I discuss how the existence of this tradeoff can help us defend (...) Hoffmann's norm for modelling. (shrink)