The market failures approach to business ethics argues that economic theory regarding the efficient workings of a market can generate normative prescriptions for managerial behaviour. It argues that actions that inhibit Pareto optimal solutions are immoral. However, the approach fails to identify goods that should be regulated or prohibited from the market, something common to the moral limits to markets approach to business ethics. There are, however, numerous assumptions underlying Paretian efficiency, including some about the preferences of market participants. Trade (...) in some goods violates some of these assumptions, and so these goods are morally suspect and can be understood to indicate that the market for these goods is not moral. This creates grounds sufficient for regulating, and possibly prohibiting, these goods. To help determine whether it is then necessary to regulate the goods, I propose a supplementary economic analysis to ascertain why an assumption regarding a particular preference is being violated. (shrink)

Suspicion of “physics envy” surrounds the standard statistical toolbox used in the empirical sciences, from biology to psychology. Mainstream methods in these fields, various lines of criticism point out, often fall short of the basic requirements of measurement. Quantitative scales are applied to variables that can hardly be treated as measurable magnitudes, like preferences or happiness; hypotheses are tested by comparing data with conventional significance thresholds that hardly mention effect sizes. This article discusses what I call “shmeasurement.” To “shmeasure” is (...) to fail to apply quantitative tools to quantitative questions. We “shmeasure” when we try to measure what cannot be measured, or, conversely, when we ask binary questions of continuous measurements. Following the critics of standard statistical tools, it is argued that our statistical toolbox is indeed less concerned with the measurement of magnitudes than we take it to be. This article adds, however, that measurement is not all there is to scientific activity. Most techniques of proof do not resemble measurement as much as voting—a practice that makes frequent use of numbers, figures, or measurements, yet is not chiefly concerned with assessing quantities. Measurement is only one among three functions of the scientific toolbox, the other two being collating observations and deciding which hypotheses to relinquish. I thus make a plea for “shmeasurement”: the mismeasure of things starts to make more sense once we take into account the nonquantitative side of scientific practice. (shrink)

The relevance to decision research of recent advances in the philosophy of social science is considered. The critical realism of Roy Bhaskar argues for the identification of contextually contingent explanatory mechanisms at multiple levels based on concepts grounded in intersubjectively shared reality. Using examples from the author’s and other’s research on the psychology of decisions involving risk and uncertainty, this paper explores the implications of taking a critical realist approach. It is argued that critical realism has the potential to advance (...) and unify disparate experimental and naturalistic lines of research. Furthermore, a diverse range of experimental, process-tracing and observational methods can play important complementary roles in developing fruitful critical realist explanations of decisions involving risk and uncertainty. (shrink)

Suspicion of “physics envy” surrounds the standard statistical toolbox used in the empirical sciences, from biology to psychology. Mainstream methods in these fields, various lines of criticism point out, often fall short of the basic requirements of measurement. Quantitative scales are applied to variables that can hardly be treated as measurable magnitudes, like preferences or happiness; hypotheses are tested by comparing data with conventional significance thresholds that hardly mention effect sizes. This article discusses what I call “shmeasurement.” To “shmeasure” is (...) to fail to apply quantitative tools to quantitative questions. We “shmeasure” when we try to measure what cannot be measured, or, conversely, when we ask binary questions of continuous measurements. Following the critics of standard statistical tools, it is argued that our statistical toolbox is indeed less concerned with the measurement of magnitudes than we take it to be. This article adds, however, that measurement is not all there is to scientific activity. Most techniques of proof do not resemble measurement as much as voting—a practice that makes frequent use of numbers, figures, or measurements, yet is not chiefly concerned with assessing quantities. Measurement is only one among three functions of the scientific toolbox, the other two being collating observations and deciding which hypotheses to relinquish. I thus make a plea for “shmeasurement”: the mismeasure of things starts to make more sense once we take into account the nonquantitative side of scientific practice. (shrink)

Increasingly, it is accepted wisdom for agricultural scientists to get feedback from indigenous peoples—peasants—about new improved seeds and biotechnologies before their official release from the experiment station. What is not yet accepted wisdom is the importance of cognitive science to research on farmer decision making, especially of the type “Why don't they adopt.” In this paper, the impact of the cognitive revolution on models of farmer decision making is described, and decision making models before and after the cognitive revolution are (...) contrasted. An example of a decision model after the cognitive revolution is given by the Malawi farmer's decision whether to use chemical fertilizers or organic fertilizers or both. Results of testing the model show that in Malawi, smallholders' lack of capital and credit are more important factors constraining use of chemical fertilizers than are indigenous beliefs in organic fertilizers or fears of a future dependency on chemicals. (shrink)

This French paper is a preliminary version of P. Mongin, "Does Optimization Imply Rationality?", Synthese, 124, 2000, p. 73-111, to which readers are referred to.

In this paper, I propose a novel theory of sacred values, which are a recently proposed type of conative attitude meant to account for religious and political actions that are incomprehensible using theories of rational choice. Sacred values are unique mental states because they encode unconditional preferences for certain privileged outcomes. I develop Isolated Sacred Value Theory by formulating two decision principles that reflect behavior in morally-relevant circumstances: the inviolability principle and the unrankability principle. Having formulated my proposal, I consider (...) the impact of my proposal on extant theories of rational choice. Lastly, I apply ISVT to problems regarding the typical extension of sacred values and the possibility of immoral actions. (shrink)

This article considers how people judge the identity of objects (e.g., how people decide that a description of an object at one time, t₀, belongs to the same object as a description of it at another time, t₁). The authors propose a causal continuer model for these judgments, based on an earlier theory by Nozick (1981). According to this model, the 2 descriptions belong to the same object if (a) the object at t₁ is among those that are causally close (...) enough to be genuine continuers of the original and (b) it is the closest of these close-enough contenders. A quantitative version of the model makes accurate predictions about judgments of which a pair of objects is identical to an original (Experiments 1 and 2). The model makes correct qualitative predictions about identity across radical disassembly (Experiment 1) as well as more ordinary transformations (Experiments 2 and 3). (shrink)

Maximization theory, which is borrowed from economics, provides techniques for predicing the behavior of animals - including humans. A theoretical behavioral space is constructed in which each point represents a given combination of various behavioral alternatives. With two alternatives - behavior A and behavior B - each point within the space represents a certain amount of time spent performing behavior A and a certain amount of time spent performing behavior B. A particular environmental situation can be described as a constraint (...) on available points (a circumscribed area) within the space. Maximization theory assumes that animals always choose the available point with the highest numerical value. The task of maximization theory is to assign to points in the behavioral space values that remain constant across various environmental situations; as those situations change, the point actually chosen is always the one with the highest assigned value. (shrink)