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Papers by Daniel Osherson

The Australasian Journal of Logic, 2015

We show that standard statistical tests for randomness of finite sequences are language-dependent... more We show that standard statistical tests for randomness of finite sequences are language-dependent in an inductively pernicious way.

The Review of Symbolic Logic, 2012

We describe a logic of preference in which modal connectives reflect reasons to desire that a sen... more We describe a logic of preference in which modal connectives reflect reasons to desire that a sentence be true. Various conditions on models are introduced and analyzed.

Thinking & Reasoning, 2014

Given a prior distribution over a finite outcome space, how is the distribution updated when one ... more Given a prior distribution over a finite outcome space, how is the distribution updated when one outcome is excluded (i.e., assigned probability 0)? We describe two experiments in which estimated probabilities seem to "stick" to salient events. The probabilities of such events remain relatively invariant through updating. Our results suggest that the credence assigned to a salient category is sometimes more basic than the credence assigned to the constituents that comprise the category. * We thank Derek Shiller for a lecture on probability that inspired the work reported here. We also thank Vincenzo Crupi, Konstantinos Hadjichristidis, David Over, and Steven Sloman for helpful comments. Contact:

Because the conjunction p-and-q implies p, the value of a bet on p-and-q cannot exceed the value ... more Because the conjunction p-and-q implies p, the value of a bet on p-and-q cannot exceed the value of a bet on p at the same stakes. We tested recognition of this principle in a betting paradigm that (a) discouraged misreading p as p-and-not-q, and (b) encouraged genuinely conjunctive reading of p-and-q. Frequent violations were nonetheless observed. The findings appear to

We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to the... more We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem recapitulates insights achieved by other investigators, and clarifies the connection of coherence and proper scoring rules to Bregman divergence.

Alternative measures of confirmation or evidential support have been proposed to express the impa... more Alternative measures of confirmation or evidential support have been proposed to express the impact of ascertaining one event on the credibility of another. We report an experiment that compares the adequacy of several such measures as descriptions of confirmation judgment in a probabilistic context. � 2006 Elsevier B.V. All rights reserved.

D ecision makers often rely on expert opinion when making forecasts under uncertainty. In doing s... more D ecision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our queries. In practice, human judges (even experts) may be incoherent, and may prefer to assess only the subset of events about which they are comfortable offering an opinion. In this paper, a new methodology is developed for combining expert assessment of chance. The method retains the conceptual and computational simplicity of linear averaging, but generalizes the standard approach by relaxing the requirements on expert elicitation. The method also enjoys provable performance guarantees, and in experiments with real-world forecasting data is shown to offer both computational efficiency and competitive forecasting gains as compared to rival aggregation methods. This paper is relevant to the practice of decision analysis, for it enables an elicitation methodology in which judges have freedom to choose the events they assess.

D ecision makers often rely on expert opinion when making forecasts under uncertainty. In doing s... more D ecision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our queries. In practice, human judges (even experts) may be incoherent, and may prefer to assess only the subset of events about which they are comfortable offering an opinion. In this paper, a new methodology is developed for combining expert assessment of chance. The method retains the conceptual and computational simplicity of linear averaging, but generalizes the standard approach by relaxing the requirements on expert elicitation. The method also enjoys provable performance guarantees, and in experiments with real-world forecasting data is shown to offer both computational efficiency and competitive forecasting gains as compared to rival aggregation methods. This paper is relevant to the practice of decision analysis, for it enables an elicitation methodology in which judges have freedom to choose the events they assess.

Games and Economic Behavior, Jul 31, 2006

We consider a panel of experts asked to assign probabilities to events, both logically simple and... more We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistically incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group's expertise.

Games and Economic Behavior, Jul 31, 2006

We consider a panel of experts asked to assign probabilities to events, both logically simple and... more We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistically incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group's expertise.

Games and Economic Behavior, Jul 31, 2006

We consider a panel of experts asked to assign probabilities to events, both logically simple and... more We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistically incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group's expertise.

We consider evidence relevant to whether a (possibly idealized) physical process is producing its... more We consider evidence relevant to whether a (possibly idealized) physical process is producing its output randomly. For definiteness, we'll consider a coin-flipper C which reports "H" for heads and "T" for tails. By C producing its output "randomly," we mean H and T have equal probability and trials are independent. If C produces its output randomly (in the above sense), then we'll say that C is a random device.

The Australasian Journal of Logic, 2015

We show that standard statistical tests for randomness of finite sequences are language-dependent... more We show that standard statistical tests for randomness of finite sequences are language-dependent in an inductively pernicious way.

The Review of Symbolic Logic, 2012

We describe a logic of preference in which modal connectives reflect reasons to desire that a sen... more We describe a logic of preference in which modal connectives reflect reasons to desire that a sentence be true. Various conditions on models are introduced and analyzed.

Thinking & Reasoning, 2014

Given a prior distribution over a finite outcome space, how is the distribution updated when one ... more Given a prior distribution over a finite outcome space, how is the distribution updated when one outcome is excluded (i.e., assigned probability 0)? We describe two experiments in which estimated probabilities seem to "stick" to salient events. The probabilities of such events remain relatively invariant through updating. Our results suggest that the credence assigned to a salient category is sometimes more basic than the credence assigned to the constituents that comprise the category. * We thank Derek Shiller for a lecture on probability that inspired the work reported here. We also thank Vincenzo Crupi, Konstantinos Hadjichristidis, David Over, and Steven Sloman for helpful comments. Contact:

Because the conjunction p-and-q implies p, the value of a bet on p-and-q cannot exceed the value ... more Because the conjunction p-and-q implies p, the value of a bet on p-and-q cannot exceed the value of a bet on p at the same stakes. We tested recognition of this principle in a betting paradigm that (a) discouraged misreading p as p-and-not-q, and (b) encouraged genuinely conjunctive reading of p-and-q. Frequent violations were nonetheless observed. The findings appear to

We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to the... more We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem recapitulates insights achieved by other investigators, and clarifies the connection of coherence and proper scoring rules to Bregman divergence.

Alternative measures of confirmation or evidential support have been proposed to express the impa... more Alternative measures of confirmation or evidential support have been proposed to express the impact of ascertaining one event on the credibility of another. We report an experiment that compares the adequacy of several such measures as descriptions of confirmation judgment in a probabilistic context. � 2006 Elsevier B.V. All rights reserved.

D ecision makers often rely on expert opinion when making forecasts under uncertainty. In doing s... more D ecision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our queries. In practice, human judges (even experts) may be incoherent, and may prefer to assess only the subset of events about which they are comfortable offering an opinion. In this paper, a new methodology is developed for combining expert assessment of chance. The method retains the conceptual and computational simplicity of linear averaging, but generalizes the standard approach by relaxing the requirements on expert elicitation. The method also enjoys provable performance guarantees, and in experiments with real-world forecasting data is shown to offer both computational efficiency and competitive forecasting gains as compared to rival aggregation methods. This paper is relevant to the practice of decision analysis, for it enables an elicitation methodology in which judges have freedom to choose the events they assess.

D ecision makers often rely on expert opinion when making forecasts under uncertainty. In doing s... more D ecision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our queries. In practice, human judges (even experts) may be incoherent, and may prefer to assess only the subset of events about which they are comfortable offering an opinion. In this paper, a new methodology is developed for combining expert assessment of chance. The method retains the conceptual and computational simplicity of linear averaging, but generalizes the standard approach by relaxing the requirements on expert elicitation. The method also enjoys provable performance guarantees, and in experiments with real-world forecasting data is shown to offer both computational efficiency and competitive forecasting gains as compared to rival aggregation methods. This paper is relevant to the practice of decision analysis, for it enables an elicitation methodology in which judges have freedom to choose the events they assess.

Games and Economic Behavior, Jul 31, 2006

We consider a panel of experts asked to assign probabilities to events, both logically simple and... more We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistically incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group's expertise.

Games and Economic Behavior, Jul 31, 2006

We consider a panel of experts asked to assign probabilities to events, both logically simple and... more We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistically incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group's expertise.

Games and Economic Behavior, Jul 31, 2006

We consider a panel of experts asked to assign probabilities to events, both logically simple and... more We consider a panel of experts asked to assign probabilities to events, both logically simple and complex. The events evaluated by different experts are based on overlapping sets of variables but may otherwise be distinct. The union of all the judgments will likely be probabilistically incoherent. We address the problem of revising the probability estimates of the panel so as to produce a coherent set that best represents the group's expertise.

We consider evidence relevant to whether a (possibly idealized) physical process is producing its... more We consider evidence relevant to whether a (possibly idealized) physical process is producing its output randomly. For definiteness, we'll consider a coin-flipper C which reports "H" for heads and "T" for tails. By C producing its output "randomly," we mean H and T have equal probability and trials are independent. If C produces its output randomly (in the above sense), then we'll say that C is a random device.