The Relationship Between Numeracy and the Value Function and Probability Weighting in Prospect Theory

2012 ◽  
Author(s):  
Mona Gauth ◽  
Maria Henriksson ◽  
Peter Juslin ◽  
Neda Kerimi ◽  
Marcus Lindskog ◽  
...  
Keyword(s):  
Prospect Theory ◽  
Value Function ◽  
Probability Weighting ◽  
The Relationship ◽  
The Value Function

Metric for Disassembly and Reuse Decisions: Formulation and Validation

2008 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Deborah Thurston
Keyword(s):  
Maximum Likelihood ◽  
Value Function ◽  
Choice Theory ◽  
Decision Makers ◽  
Maximum Likelihood Estimates ◽  
Likelihood Method ◽  
Maximum Value ◽  
Long Range Planning ◽  
The Relationship ◽  
The Value Function

Design for disassembly and reuse focuses on developing methods to minimize difficulty in disassembly for maintenance or reuse. These methods can gain substantially if the relationship between component attributes (material mix, ease of disassembly etc.) and their likelihood of reuse or disposal is understood. For products already in the marketplace, a feedback approach that evaluates willingness of manufacturers or customers (decision makers) to reuse a component can reveal how attributes of a component affect reuse decisions. This paper introduces some metrics and combines them with ones proposed in literature into a measure that captures the overall value of a decision made by the decision makers. The premise is that the decision makers would choose a decision that has the maximum value. Four decisions are considered regarding a component’s fate after recovery ranging from direct reuse to disposal. A method on the lines of discrete choice theory is utilized that uses maximum likelihood estimates to determine the parameters that define the value function. The maximum likelihood method can take inputs from actual decisions made by the decision makers to assess the value function. This function can be used to determine the likelihood that the component takes a certain path (one of the four decisions), taking as input its attributes, which can facilitate long range planning and also help determine ways reuse decisions can be influenced.

scholarly journals A DECISION-MAKING FRAMEWORK BASED ON THE PROSPECT THEORY UNDER AN INTUITIONISTIC FUZZY ENVIRONMENT

2018 ◽  
Vol 24 (6) ◽  
pp. 2374-2396 ◽  
Author(s):  
Jing Gu ◽  
Zijian Wang ◽  
Zeshui Xu ◽  
Xuezheng Chen
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Value Function ◽  
Behavior Pattern ◽  
Weighting Function ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Decision Making Framework ◽  
Decision Making Behavior ◽  
The Value Function

Uncertainty and ambiguity are frequently involved in the decision-making process in our daily life. This paper develops a generalized decision-making framework based on the prospect theory under an intuitionistic fuzzy environment, by closely integrating the prospect theory and the intuitionistic fuzzy sets into our framework. We demonstrate how to compute the intuitionistic fuzzy prospect values as the reference values for decision-making and elaborate a four-step editing phase and a valuation phase with two key functions: the value function and the weighting function. We then conduct experiments to test our decision- making methodology and the key features of our framework. The experimental results show that the shapes of the value function and the weighting function in our framework are in line with those of prospect theory. The methodology proposed in this paper to elicit prospects that are not only under uncertainty but also under ambiguity. We reveal the decision-making behavior pattern through comparing the parameters. People are less risk averse when making decisions under an intuitionistic fuzzy environment than under uncertainty. People still underestimate the probability of the events in our experiment. Further, the choices of participants in the experiments are consistent with the addition and multiplication principles of our framework.

An Empirical Study on Value Functions of Stock Markets

2011 ◽  
Vol 204-210 ◽  
pp. 899-906
Author(s):  
Feng Hua Wen ◽  
Gui Tian Rao ◽  
Xiao Guang Yang
Keyword(s):  
Prospect Theory ◽  
Stock Markets ◽  
Subjective Experience ◽  
Value Function ◽  
Empirical Studies ◽  
Value Functions ◽  
A Value ◽  
Egarch Model ◽  
The Value Function ◽  
Psychological Experiments

As a core component of the prospect theory, a value function is employed to characterize the subjective experience of a decision-maker’s gain or loss. Previous empirical studies of the prospect theory were largely carried out through psychological experiments on individual decision-makers. In this paper, taking the whole stock market as an entity, we use the flow of information extracted by EGARCH Model as the proxy variable of change in wealth, and then use a two-stage power function as the representation of the value function to study the daily return data from the stock markets of 10 countries or regions. Empirical results show that the value functions of all the 10 stock markets present the shape of inverse-S, instead of the S-Shape of the value function generated by most psychological experiments on individuals.

scholarly journals Nonsmooth optimal regulation and discontinuous stabilization

2003 ◽  
Vol 2003 (20) ◽  
pp. 1159-1195 ◽  
Author(s):  
A. Bacciotti ◽  
F. Ceragioli
Keyword(s):  
Control Systems ◽  
Value Function ◽  
Infinite Horizon ◽  
Optimal Regulation ◽  
Affine Control Systems ◽  
Feedback Laws ◽  
The Relationship ◽  
The Value Function

For affine control systems, we study the relationship between an optimal regulation problem on the infinite horizon and stabilizability. We are interested in the case the value function of the optimal regulation problem is not smooth and feedback laws involved in stabilizability may be discontinuous.

Can prospect theory explain the disposition effect? An analysis based on value function

2018 ◽  
Vol 8 (3) ◽  
pp. 235-255
Author(s):  
Xiaotian Liu ◽  
Huayue Zhang ◽  
Shengmin Zhao
Keyword(s):  
Prospect Theory ◽  
Portfolio Choice ◽  
Crucial Role ◽  
Value Function ◽  
Disposition Effect ◽  
Dynamic Programming Method ◽  
Prediction Ability ◽  
Content Type ◽  
The Impact ◽  
The Value Function

Purpose The prospect theory is potentially an essential ingredient in modeling the disposition effect. However, many scholars have tried to explain the disposition effect with the help of prospect theory and they came to opposite conclusions. The purpose of this paper is to examine the impact of value function of the prospect theory on predicting the disposition effect. Design/methodology/approach Lagrange multiplier optimization and dynamic programming method are used to solve the representative investor’s optimal portfolio choice problem. Furthermore, numerical simulation is used to compare the prediction ability of different types of value function. Findings The authors support that the value function has a crucial role in predicting the disposition effect with prospect theory, i.e. the curvature and boundedness of the value function may influence the performance of applying the prospect theory in the disposition effect. They conclude that a piecewise negative exponential value function can predict the disposition effect, while others like the piecewise power value function may not. Originality/value Extant literature about modeling the disposition effect with the prospect theory mostly focus on the time when gain-loss utility occurs or the selection of reference point. This paper based on the value function properties provides a new perspective in analyzing the crucial role that value function has in predicting financial market anomalies.

The value function for time-related decisions

2011 ◽  
Author(s):  
Anouk Festjens ◽  
Siegfried Dewitte ◽  
Enrico Diecidue ◽  
Sabrina Bruyneel
Keyword(s):  
Value Function ◽  
The Value Function
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