The problem known as Buridan’s Ass says that a hungry donkey equipoised between two identical bales of hay will starve to death. Indecision kills the ass. Some philosophers worry about human analogs. Computer scientists since the 1960s have known about the computer versions of such cases. From what Leslie Lamport calls ‘Buridan’s Principle’—a discrete decision based on a continuous range of input-values cannot be made in a bounded time—it follows that the possibilities for human analogs of Buridan’s Ass are far (...) more wide-ranging and securely provable than has been acknowledged in philosophy. We are never necessarily decisive. This is mathematically provable. I explore four consequences: first, increased interest of the literature’s solutions to Buridan’s Ass; second, a new asymmetry between responsibility for omissions and responsibility for actions; third, clarification of the standard account of akrasia; and, fourth, clarification of the role of credences in normative decision-theory. (shrink)

Self-control requires intentionally resisting what we most want to do. Yet we do what we most want to do, if we do anything intentionally at that time (The Law of Desire). Therefore, self-control is impossible. So runs a well-studied puzzle. The three standard accounts assume that if a desire is our strongest desire, then it is stronger than all others. But that assumption is false. For we may have desires of equal strength. I describe cases which feature tied desires, self-control, (...) and doing what we most want to do. Following insights from computer science (Buridan’s Principle), equal-strength desires are possible any time we try to decide. My proposal may also be used to bolster all three standard analyses. (shrink)

Diffusion models are widely-used and successful accounts of the time course of two-choice decision making. Most diffusion models assume constant boundaries, which are the threshold levels of evidence that must be sampled from a stimulus to reach a decision. We summarize theoretical results from statistics that relate distributions of decisions and response times to diffusion models with time-varying boundaries. We then develop a computational method for finding time-varying boundaries from empirical data, and apply our new method to two problems. The (...) first problem involves finding the time-varying boundaries that make diffusion models equivalent to the alternative sequential sampling class of accumulator models. The second problem involves finding the time-varying boundaries, at the individual level, that best fit empirical data for perceptual stimuli that provide equal evidence for both decision alternatives. We discuss the theoretical and modeling implications of using time-varying boundaries in diffusion models, as well as the limitations and potential of our approach to their inference. (shrink)