Nondeterminism and Probabilistic Choice: Obeying the Laws

2000 ◽  
pp. 350-365 ◽  
Author(s):  
Michael Mislove
Keyword(s):  
Probabilistic Choice

Econometric Models for Probabilistic Choice Among Products

1980 ◽  
Vol 53 (S3) ◽  
pp. S13 ◽  
Author(s):  
Daniel McFadden
Keyword(s):  
Econometric Models ◽  
Probabilistic Choice

Falsification of Hybrid Systems Using Adaptive Probabilistic Search

2021 ◽  
Vol 31 (3) ◽  
pp. 1-22
Author(s):  
Gidon Ernst ◽  
Sean Sedwards ◽  
Zhenya Zhang ◽  
Ichiro Hasuo
Keyword(s):  
Hybrid Systems ◽  
Input Signal ◽  
Tree Search ◽  
Single Trial ◽  
Decision Point ◽  
Directed Tree ◽  
Probabilistic Choice ◽  
Original Algorithm ◽  
Fine Grained ◽  
Probabilistic Search

We present and analyse an algorithm that quickly finds falsifying inputs for hybrid systems. Our method is based on a probabilistically directed tree search, whose distribution adapts to consider an increasingly fine-grained discretization of the input space. In experiments with standard benchmarks, our algorithm shows comparable or better performance to existing techniques, yet it does not build an explicit model of a system. Instead, at each decision point within a single trial, it makes an uninformed probabilistic choice between simple strategies to extend the input signal by means of exploration or exploitation. Key to our approach is the way input signal space is decomposed into levels, such that coarse segments are more probable than fine segments. We perform experiments to demonstrate how and why our approach works, finding that a fully randomized exploration strategy performs as well as our original algorithm that exploits robustness. We propose this strategy as a new baseline for falsification and conclude that more discriminative benchmarks are required.

scholarly journals Primate prefrontal neurons signal economic risk derived from the statistics of recent reward experience

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Fabian Grabenhorst ◽  
Ken-Ichiro Tsutsui ◽  
Shunsuke Kobayashi ◽  
Wolfram Schultz
Keyword(s):  
Dorsolateral Prefrontal Cortex ◽  
Choice Task ◽  
Support Vector ◽  
Probabilistic Choice ◽  
Economic Decisions ◽  
Decision Variables ◽  
Dorsolateral Prefrontal ◽  
Process Risk ◽  
The Brain ◽  
Statistical Variance

Risk derives from the variation of rewards and governs economic decisions, yet how the brain calculates risk from the frequency of experienced events, rather than from explicit risk-descriptive cues, remains unclear. Here, we investigated whether neurons in dorsolateral prefrontal cortex process risk derived from reward experience. Monkeys performed in a probabilistic choice task in which the statistical variance of experienced rewards evolved continually. During these choices, prefrontal neurons signaled the reward-variance associated with specific objects (‘object risk’) or actions (‘action risk’). Crucially, risk was not derived from explicit, risk-descriptive cues but calculated internally from the variance of recently experienced rewards. Support-vector-machine decoding demonstrated accurate neuronal risk discrimination. Within trials, neuronal signals transitioned from experienced reward to risk (risk updating) and from risk to upcoming choice (choice computation). Thus, prefrontal neurons encode the statistical variance of recently experienced rewards, complying with formal decision variables of object risk and action risk.

scholarly journals Mice tune out not in: Violation of prediction drives auditory saliency

2019 ◽  
Author(s):  
Meike M. Rogalla ◽  
Inga Rauser ◽  
Karsten Schulze ◽  
Lasse Osterhagen ◽  
K Jannis Hildebrandt
Keyword(s):  
Auditory Processing ◽  
Choice Model ◽  
Short Term ◽  
Probabilistic Choice ◽  
Auditory Detection ◽  
Irrelevant Distractors ◽  
Serial Correlations ◽  
Low Probability ◽  
Psychophysical Studies

SummarySuccessful navigation in complex acoustic scenes requires focusing on relevant sounds while ignoring irrelevant distractors. It has been argued that the ability to track stimulus statistics and generate predictions supports the choice what to attend and what to ignore. However, the role of these predictions about future auditory events in drafting decisions remains elusive. While most psychophysical studies in humans indicate that expected stimuli serve as implicit cues attracting attention, most work studying physiological auditory processing in animals highlights the detection of unexpected, surprising stimuli. Here, we tested whether in the mouse, target probability is used as an implicit cue attracting attention or whether detection is biased towards low-probability deviants using an auditory detection task. We implemented a probabilistic choice model to investigate whether a possible dependence on stimulus statistics arises from short term serial correlations or from integration over longer periods. Our results demonstrate that target detectability in mice decreases with increasing probability, contrary to humans. We suggest that mice indeed track probability over a time scale of at least several minutes but do not use this information in the same way as humans do: instead of maximizing reward by focusing on high-probability targets, the saliency of a target is determined by surprise.

Estimating Lost Sales for Substitutable Products with Uncertain On-Shelf Availability

2021 ◽  
Author(s):  
Daniel Steeneck ◽  
Fredrik Eng-Larsson ◽  
Francisco Jauffred
Keyword(s):  
Inventory Management ◽  
Product Category ◽  
Problem Definition ◽  
Lost Sales ◽  
Negative Consequences ◽  
Demand Model ◽  
Data Set ◽  
Probabilistic Choice ◽  
Substitutable Products ◽  
Choice Sets

Problem definition: We address the problem of how to estimate lost sales for substitutable products when there is no reliable on-shelf availability (OSA) information. Academic/practical relevance: We develop a novel approach to estimating lost sales using only sales data, a market share estimate, and an estimate of overall availability. We use the method to illustrate the negative consequences of using potentially inaccurate inventory records as indicators of availability. Methodology: We suggest a partially hidden Markov model of OSA to generate probabilistic choice sets and incorporate these probabilistic choice sets into the estimation of a multinomial logit demand model using a nested expectation-maximization algorithm. We highlight the importance of considering inventory reliability problems first through simulation and then by applying the procedure to a data set from a major U.S. retailer. Results: The simulations show that the method converges in seconds and produces estimates with similar or lower bias than state-of-the-art benchmarks. For the product category under consideration at the retailer, our procedure finds lost sales of around 3.0% compared with 0.2% when relying on the inventory record as an indicator of availability. Managerial implications: The method efficiently computes estimates that can be used to improve inventory management and guide managers on how to use their scarce resources to improve stocking execution. The research also shows that ignoring inventory record inaccuracies when estimating lost sales can produce substantially inaccurate estimates, which leads to incorrect parameters in supply chain planning.

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