This volume is the result of a conference held at the University of California, Irvine, on the topics that provide its title -- choice, decision, and measurement. The conference was planned, and the volume prepared, in honor of Professor R. Duncan Luce on his 70th birthday. Following a short autobiographical statement by Luce, the volume is organized into four topics, to each of which Luce has made significant contributions. The book provides an overview of current issues in each area and presents some of the best recent theoretical and empirical work. Personal reflections on Luce and his work begin each section. These reflections were written by outstanding senior researchers: Peter Fishburn (Preference and Decision Making), Patrick Suppes (Measurement Theory and Axiomatic Systems), William J. McGill (Psychophysics and Reaction Time), and W.K. Estes (Choice, Identification and Categorization). The first section presents recent theoretical and empirical work on descriptive models of decision making, and theoretical results on general probabilistic models of choice and ranking. Luce's recent theoretical and empirical work on rank- and sign-dependent utility theory is important in many of these contributions. The second section presents results from psychophysics, probabilistic measurement, aggregation of expert opinion, and test theory. The third section presents various process oriented models, with supportive data, for tasks such as redundant signal detection, forced choice, and absolute identification. The final section contains theory and data on categorization and attention, and general theoretical results for developing and testing models in these domains.

Behavioral Social Choice looks at the probabilistic foundations of collective decision-making rules. The authors challenge much of the existing theoretical wisdom about social choice processes, and seek to restore faith in the possibility of democratic decision-making. In particular, they argue that worries about the supposed prevalence of majority rule cycles that would preclude groups from reaching a final decision about what alternative they prefer have been greatly overstated. In practice, majority rule can be expected to work well in most real-world settings. Furthermore, if there is a problem, they show that the problem is more likely to be one of sample estimates missing the majority winner in a close contest (e.g., Bush-Gore) than a problem about cycling. The authors also provide new mathematical tools to estimate the prevalence of cycles as a function of sample size and insights into how alternative model specifications can change our estimates of social orderings.

Behavioral Social Choice looks at the probabilistic foundations of collective decision-making rules. The authors challenge much of the existing theoretical wisdom about social choice processes, and seek to restore faith in the possibility of democratic decision-making. In particular, they argue that worries about the supposed prevalence of majority rule cycles that would preclude groups from reaching a final decision about what alternative they prefer have been greatly overstated. In practice, majority rule can be expected to work well in most real-world settings. Furthermore, if there is a problem, they show that the problem is more likely to be one of sample estimates missing the majority winner in a close contest (e.g., Bush-Gore) than a problem about cycling. The authors also provide new mathematical tools to estimate the prevalence of cycles as a function of sample size and insights into how alternative model specifications can change our estimates of social orderings.

Best-worst scaling (BWS) is an extension of the method of paired comparison to multiple choices that asks participants to choose both the most and the least attractive options or features from a set of choices. It is an increasingly popular way for academics and practitioners in social science, business, and other disciplines to study and model choice. This book provides an authoritative and systematic treatment of best-worst scaling, introducing readers to the theory and methods for three broad classes of applications. It uses a variety of case studies to illustrate simple but reliable ways to design, implement, apply, and analyze choice data in specific contexts, and showcases the wide range of potential applications across many different disciplines. Best-worst scaling avoids many rating scale problems and will appeal to those wanting to measure subjective quantities with known measurement properties that can be easily interpreted and applied.

This volume is the result of a conference held at the University of California, Irvine, on the topics that provide its title -- choice, decision, and measurement. The conference was planned, and the volume prepared, in honor of Professor R. Duncan Luce on his 70th birthday. Following a short autobiographical statement by Luce, the volume is organized into four topics, to each of which Luce has made significant contributions.
The book provides an overview of current issues in each area and presents some of the best recent theoretical and empirical work. Personal reflections on Luce and his work begin each section. These reflections were written by outstanding senior researchers: Peter Fishburn (Preference and Decision Making), Patrick Suppes (Measurement Theory and Axiomatic Systems), William J. McGill (Psychophysics and Reaction Time), and W.K. Estes (Choice, Identification and Categorization).
The first section presents recent theoretical and empirical work on descriptive models of decision making, and theoretical results on general probabilistic models of choice and ranking. Luce's recent theoretical and empirical work on rank- and sign-dependent utility theory is important in many of these contributions. The second section presents results from psychophysics, probabilistic measurement, aggregation of expert opinion, and test theory. The third section presents various process oriented models, with supportive data, for tasks such as redundant signal detection, forced choice, and absolute identification. The final section contains theory and data on categorization and attention, and general theoretical results for developing and testing models in these domains.