scholarly journals The effect of model uncertainty on cooperation in sensorimotor interactions

2013 ◽  
Vol 10 (87) ◽  
pp. 20130554 ◽  
Author(s):  
J. Grau-Moya ◽  
E. Hez ◽  
G. Pezzulo ◽  
D. A. Braun
Keyword(s):  
Model Uncertainty ◽  
Probabilistic Models ◽  
Important Determinant ◽  
Human Subjects ◽  
Risk Sensitivity ◽  
Cooperative Solution ◽  
Worst Case ◽  
Uncertainty Model ◽  
Risk Sensitive ◽  
Sensorimotor Interactions

Decision-makers have been shown to rely on probabilistic models for perception and action. However, these models can be incorrect or partially wrong in which case the decision-maker has to cope with model uncertainty. Model uncertainty has recently also been shown to be an important determinant of sensorimotor behaviour in humans that can lead to risk-sensitive deviations from Bayes optimal behaviour towards worst-case or best-case outcomes. Here, we investigate the effect of model uncertainty on cooperation in sensorimotor interactions similar to the stag-hunt game, where players develop models about the other player and decide between a pay-off-dominant cooperative solution and a risk-dominant, non-cooperative solution. In simulations, we show that players who allow for optimistic deviations from their opponent model are much more likely to converge to cooperative outcomes. We also implemented this agent model in a virtual reality environment, and let human subjects play against a virtual player. In this game, subjects' pay-offs were experienced as forces opposing their movements. During the experiment, we manipulated the risk sensitivity of the computer player and observed human responses. We found not only that humans adaptively changed their level of cooperation depending on the risk sensitivity of the computer player but also that their initial play exhibited characteristic risk-sensitive biases. Our results suggest that model uncertainty is an important determinant of cooperation in two-player sensorimotor interactions.

scholarly journals Learning Speech Production and Perception through Sensorimotor Interactions

2020 ◽  
Author(s):  
Shihab Shamma ◽  
Prachi Patel ◽  
Shoutik Mukherjee ◽  
Guilhem Marion ◽  
Bahar Khalighinejad ◽  
...  
Keyword(s):  
Vocal Tract ◽  
Human Subjects ◽  
Motor Task ◽  
Neural Processes ◽  
Mathematical Simulations ◽  
Predictive Role ◽  
Sensorimotor Interactions ◽  
Forward Projection ◽  
Cortical Regions

Abstract Action and Perception are closely linked in many behaviors necessitating a close coordination between sensory and motor neural processes so as to achieve a well-integrated smoothly evolving task performance. To investigate the detailed nature of these sensorimotor interactions, and their role in learning and executing the skilled motor task of speaking, we analyzed ECoG recordings of responses in the high-γ band (70 Hz-150 Hz) in human subjects while they listened to, spoke, or silently articulated speech. We found elaborate spectrotemporally-modulated neural activity projecting in both forward (motor-to-sensory) and inverse directions between the higher-auditory and motor cortical regions engaged during speaking. Furthermore, mathematical simulations demonstrate a key role for the forward projection in learning to control the vocal tract, beyond its commonly-postulated predictive role during execution. These results therefore offer a broader view of the functional role of the ubiquitous forward projection as an important ingredient in learning, rather than just control, of skilled sensorimotor tasks.

Competitive Pricing and Remanufacturing Problem in an Uncertain Closed-Loop Supply Chain with Risk-Sensitive Retailers

2018 ◽  
Vol 35 (01) ◽  
pp. 1850003 ◽  
Author(s):  
Hua Ke ◽  
Yong Wu ◽  
Hu Huang
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Historical Data ◽  
Risk Sensitivity ◽  
Risk Averse ◽  
Closed Loop Supply Chain ◽  
Academic Fields ◽  
Risk Sensitive ◽  
The Face ◽  
Competitive Pricing

Nowadays, pricing and remanufacturing problems under uncertain markets have gained increasing attention from both industrial and academic fields. In the literature, it is generally assumed that all the channel members are risk-neutral, ignoring the influences of channel members’ risk attitudes in the face of dynamic market. This paper focuses on a pricing problem in a closed-loop supply chain (CLSC) with two competitive risk-sensitive retailers under uncertain environment. The uncertainty is associated with the recycling costs, consumer demands and remanufacturing costs. Due to the dynamic market, supply chain managers may be unable to collect enough historical data to estimate these demands and costs when making pricing and remanufacturing decisions. In such cases, experts’ estimations are usually employed to describe these uncertain parameters. To deal with these human estimations, an uncertainty theory-based model is proposed. Based on the equilibrium results, how the retailers’ risk sensitivity and human estimations (uncertain degrees) affect the prices and profits is analyzed. It is found that both the retailers will get lower profits while the manufacturer will gain more profit when either of the two retailers becomes more risk-averse. We also find that a higher level of uncertainty in the supply chain will induce a higher collecting rate.

Robust Optimization With Mixed Interval and Probabilistic Parameter Uncertainties, Model Uncertainty, and Metamodeling Uncertainty

2019 ◽  
Author(s):  
Yanjun Zhang ◽  
Tingting Xia ◽  
Mian Li
Keyword(s):  
Robust Optimization ◽  
Real World ◽  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Optimization Problems ◽  
Probability Distributions ◽  
Parameter Uncertainties ◽  
True Value ◽  
Uncertainty Model ◽  
The Difference

Abstract Various types of uncertainties, such as parameter uncertainty, model uncertainty, metamodeling uncertainty may lead to low robustness. Parameter uncertainty can be either epistemic or aleatory in physical systems, which have been widely represented by intervals and probability distributions respectively. Model uncertainty is formally defined as the difference between the true value of the real-world process and the code output of the simulation model at the same value of inputs. Additionally, metamodeling uncertainty is introduced due to the usage of metamodels. To reduce the effects of uncertainties, robust optimization (RO) algorithms have been developed to obtain solutions being not only optimal but also less sensitive to uncertainties. Based on how parameter uncertainty is modeled, there are two categories of RO approaches: interval-based and probability-based. In real-world engineering problems, both interval and probabilistic parameter uncertainties are likely to exist simultaneously in a single problem. However, few works have considered mixed interval and probabilistic parameter uncertainties together with other types of uncertainties. In this work, a general RO framework is proposed to deal with mixed interval and probabilistic parameter uncertainties, model uncertainty, and metamodeling uncertainty simultaneously in design optimization problems using the intervals-of-statistics approaches. The consideration of multiple types of uncertainties will improve the robustness of optimal designs and reduce the risk of inappropriate decision-making, low robustness and low reliability in engineering design. Two test examples are utilized to demonstrate the applicability and effectiveness of the proposed RO approach.

Robust Optimization With Parameter and Model Uncertainties Using Gaussian Processes With Limited Samples

2017 ◽  
Author(s):  
Yanjun Zhang ◽  
Mian Li
Keyword(s):  
Robust Optimization ◽  
Engineering Design ◽  
Gaussian Processes ◽  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Computational Cost ◽  
Random Errors ◽  
Model Uncertainties ◽  
Uncertainty Model ◽  
Gp Model

Uncertainty is inevitable in engineering design. The existence of uncertainty may change the optimality and/or the feasibility of the obtained optimal solutions. In simulation-based engineering design, uncertainty could have various types of sources, such as parameter uncertainty, model uncertainty, and other random errors. To deal with uncertainty, robust optimization (RO) algorithms are developed to find solutions which are not only optimal but also robust with respect to uncertainty. Parameter uncertainty has been taken care of by various RO approaches. While model uncertainty has been ignored in majority of existing RO algorithms with the hypothesis that the simulation model used could represent the real physical system perfectly. In the authors’ earlier work, a RO framework was proposed to consider both parameter and model uncertainties using the Bayesian approach with Gaussian processes (GP), where metamodeling uncertainty introduced by GP modeling is ignored by assuming the constructed GP model is accurate enough with sufficient training samples. However, infinite samples are impossible for real applications due to prohibitive time and/or computational cost. In this work, a new RO framework is proposed to deal with both parameter and model uncertainties using GP models but only with limited samples. The compound effect of parameter, model, and metamodeling uncertainties is derived with the form of the compound mean and variance to formulate the proposed RO approach. The proposed RO approach will reduce the risk for the obtained robust optimal designs considering parameter and model uncertainties becoming non-optimal and/or infeasible due to insufficiency of samples for GP modeling. Two test examples with different degrees of complexity are utilized to demonstrate the applicability and effectiveness of the proposed approach.

scholarly journals AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models

2008 ◽  
Vol 33 ◽  
pp. 465-519 ◽  
Author(s):  
R. Mateescu ◽  
R. Dechter ◽  
R. Marinescu
Keyword(s):  
Graphical Models ◽  
Probabilistic Models ◽  
Graphical Model ◽  
Search Algorithm ◽  
Decision Diagrams ◽  
Worst Case ◽  
Ordered Binary Decision Diagrams ◽  
Search Spaces ◽  
Function Decomposition ◽  
Decomposition Structure

Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled data structures to general weighted graphical models (e.g., probabilistic models). We present the AND/OR Multi-Valued Decision Diagram (AOMDD) which compiles a graphical model into a canonical form that supports polynomial (e.g., solution counting, belief updating) or constant time (e.g. equivalence of graphical models) queries. We provide two algorithms for compiling the AOMDD of a graphical model. The first is search-based, and works by applying reduction rules to the trace of the memory intensive AND/OR search algorithm. The second is inference-based and uses a Bucket Elimination schedule to combine the AOMDDs of the input functions via the the APPLY operator. For both algorithms, the compilation time and the size of the AOMDD are, in the worst case, exponential in the treewidth of the graphical model, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs). We introduce the concept of semantic treewidth, which helps explain why the size of a decision diagram is often much smaller than the worst case bound. We provide an experimental evaluation that demonstrates the potential of AOMDDs.

scholarly journals The development and effect of risk sensitivity improvement program for driver

2015 ◽  
Vol 28 (1) ◽  
pp. 61-74
Author(s):  
Soonyeol Lee
Keyword(s):  
Training Program ◽  
Education Program ◽  
Repeated Measures ◽  
Control Group ◽  
Risk Sensitivity ◽  
Improvement Program ◽  
Risk Sensitive ◽  
Sensitivity Level ◽  
Sensitivity Improvement ◽  
Experimental Group

This study developed Risk Sensitivity Improvement program for Driver. A driver 62 people were developing a training program which can improve risk sensitivity factors was verified its effectiveness. By separating the driver from the control group and the experimental group populations was performed education program training a session, when compared to the risk sensitivity level before and 2 weeks after training showed significant improvements in risk sensitivity. The degree of risk sensitivity is improved risk sensitivity(Risk Sensitivity: RS) measure item were verified by. Risk sensitivity measure item was measured part of the risk perception and emotional anxiety. Control group and experimental group risk sensitivity level prior to performing risk sensitivity enhancement training program showed the same level. Repeated measures ANOVA and paired sample results to verify the effectiveness of using the t-test, the experimental group performed improving the risk sensitivity of a single session education program showed significant improvement in risk-sensitive than the control group. The risk-sensitive development programs through improved education could verify that it can be an effective training program that can make a difference in risk driving behavior of the driver.

scholarly journals Mediating Between Contact Feasibility and Robustness of Trajectory Optimization Through Chance Complementarity Constraints

2022 ◽  
Vol 8 ◽  
Author(s):  
Luke Drnach ◽  
John Z. Zhang ◽  
Ye Zhao
Keyword(s):  
Motion Planning ◽  
Constraint Satisfaction ◽  
Model Uncertainty ◽  
Trajectory Optimization ◽  
Chance Constraints ◽  
Complementarity Constraints ◽  
Trade Off ◽  
Block System ◽  
Risk Sensitive ◽  
Intermittent Contact

As robots move from the laboratory into the real world, motion planning will need to account for model uncertainty and risk. For robot motions involving intermittent contact, planning for uncertainty in contact is especially important, as failure to successfully make and maintain contact can be catastrophic. Here, we model uncertainty in terrain geometry and friction characteristics, and combine a risk-sensitive objective with chance constraints to provide a trade-off between robustness to uncertainty and constraint satisfaction with an arbitrarily high feasibility guarantee. We evaluate our approach in two simple examples: a push-block system for benchmarking and a single-legged hopper. We demonstrate that chance constraints alone produce trajectories similar to those produced using strict complementarity constraints; however, when equipped with a robust objective, we show the chance constraints can mediate a trade-off between robustness to uncertainty and strict constraint satisfaction. Thus, our study may represent an important step towards reasoning about contact uncertainty in motion planning.

scholarly journals Early-Stage Uncertainty: Effects of Robust Convex Optimization on Design Exploration

2020 ◽  
Author(s):  
Priya P. Pillai ◽  
Edward Burnell ◽  
Xiqing Wang ◽  
Maria C. Yang
Keyword(s):  
Robust Optimization ◽  
Design Space Exploration ◽  
Early Stage ◽  
Human Subjects ◽  
Model Development ◽  
Pareto Frontier ◽  
Design Parameters ◽  
Safety Factors ◽  
Worst Case ◽  
Robust Convex Optimization

Abstract Engineers design for an inherently uncertain world. In the early stages of design processes, they commonly account for such uncertainty either by manually choosing a specific worst-case and multiplying uncertain parameters with safety factors or by using Monte Carlo simulations to estimate the probabilistic boundaries in which their design is feasible. The safety factors of this first practice are determined by industry and organizational standards, providing a limited account of uncertainty; the second practice is time intensive, requiring the development of separate testing infrastructure. In theory, robust optimization provides an alternative, allowing set based conceptualizations of uncertainty to be represented during model development as optimizable design parameters. How these theoretical benefits translate to design practice has not previously been studied. In this work, we analyzed present use of geometric programs as design models in the aerospace industry to determine the current state-of-the-art, then conducted a human-subjects experiment to investigate how various mathematical representations of uncertainty affect design space exploration. We found that robust optimization led to far more efficient explorations of possible designs with only small differences in an experimental participant’s understanding of their model. Specifically, the Pareto frontier of a typical participant using robust optimization left less performance “on the table” across various levels of risk than the very best frontiers of participants using industry-standard practices.

scholarly journals The Origin of Behavior

2011 ◽  
Vol 01 (01) ◽  
pp. 55-108 ◽  
Author(s):  
Thomas J. Brennan ◽  
Andrew W. Lo
Keyword(s):  
Reproductive Success ◽  
Loss Aversion ◽  
Human Subjects ◽  
Initial Population ◽  
Probability Matching ◽  
Choice Problem ◽  
Risk Sensitive ◽  
Exponential Rates ◽  
The Individual ◽  
Risk Sensitive Foraging

We propose a single evolutionary explanation for the origin of several behaviors that have been observed in organisms ranging from ants to human subjects, including risk-sensitive foraging, risk aversion, loss aversion, probability matching, randomization, and diversification. Given an initial population of individuals, each assigned a purely arbitrary behavior with respect to a binary choice problem, and assuming that offspring behave identically to their parents, only those behaviors linked to reproductive success will survive, and less reproductively successful behaviors will disappear at exponential rates. When the uncertainty in reproductive success is systematic, natural selection yields behaviors that may be individually sub-optimal but are optimal from the population perspective; when reproductive uncertainty is idiosyncratic, the individual and population perspectives coincide. This framework generates a surprisingly rich set of behaviors, and the simplicity and generality of our model suggest that these derived behaviors are primitive and nearly universal within and across species.

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