Application Of Prospect Theory Research Articles

Exploring the impact of boundedly rational power plant investment decision-making by applying prospect theory

Exploring the impact of boundedly rational power plant investment decision-making by applying prospect theory

The Prospect Theory and The Stock Market

This paper is aimed to investigate the important principle of behavioral economics , prospect theory and applies prospect theory to stock market. Based on the former studies, there are three features at the core of prospect theory, and they are in turn the reference point, a principle of diminishing sensitivity and loss aversion. Prospect theory focus on how people make decisions in an uncertain situation and it combines psychology to research economic man's choice behavior rather than research rational people’s economic behavior like expected utility theory. Therefore, the prospect theory has more value and more practical to apply in the stock market and be studied to predict investors behavior. In this essay, case analysis method and theoretical analysis method will be used to explore the prospect theory and stock market. It found that prospect theory can conduct investors gain more profit because of stock reverse transaction strategy. And loss aversion, the most important factor of reversal effect, point out that when face the uncertainty, people won’t keep completely rational as the utility theory said. What’s more, it suggests the exist of reverse effect in Chinese stock market. The research contributed to present understanding of the prospect theory in China's stock market, and further explores that the stock reversal strategy is more conducive to investors' profits in China's stock market. A large part of the reasons for this phenomenon can be explained by prospect theory, while expected utility theory cannot.

Defense scheme against advanced persistent threats in mobile fog computing security

In mobile fog computing (MFC), data processing and applications are concentrated in devices at the network’s edge. This means that data can be processed in local intelligent devices without being sent to the cloud for execution, bringing great convenience to end-users. Fog computing is a semi-virtualised service computing architecture model between cloud computing and personal computing. However, its virtualized environment is vulnerable to advanced persistent threats (APT). In this paper, we propose an APT defense scheme based on MFC’s Double Q-learning (DQL) algorithm. First, we apply prospect theory (PT) to build a static subjective game model between APT attackers and legitimate users. In addition, a dynamic game scheme based on DQL is proposed against APT attacks. Finally, we compare our proposed method with the existing methods, i.e., the Q-learning algorithm, Sarsa algorithm and Greedy algorithm. The experimental results show that the proposed method can effectively suppress the attack motivation of APT attackers, improve the utility of legitimate users, and protect the security of the fog computing environment.

Supervisory Styles, Stress, and Decision Making: An Application of Prospect Theory

Abstract
 The purpose of our paper was to investigate the influence of supervisory styles on the manager's perceived stress and decision making. Supervisory styles are a combination of the leadership dimensions, consideration and initiation structure behaviors, and performance measurements, objective and subjective. We used a within-subjects experimental structure to determine the relative effects of perceived stress while making a decision under each supervisory style as well as relative risk-taking under each supervisory style. We found that managers whose supervisors use considerate leadership styles make riskier decisions than managers whose supervisors use initiates structure leadership styles. Furthermore, managers whose organizations use objective performance measures will report more stress than managers whose organizations use subjective measures. The initiates structure with objective performance measures supervisory style induced more perceived stress and resulted in lower risk-taking by the subjects than the other supervisory style combinations. Our research provides evidence that combinations of team-level leadership and organization level performance measures combine to produce unintended outcomes from the firm's managers.

Application of Prospect Theory in the Context of Predictive Maintenance Optimization Based on Risk Assessment

The optimization of predictive maintenance relies mainly on the reduction of costs and risks, which can be of various types. The evaluation of risks cannot be realized independently of the psychology state and cognitive knowledge of the decision maker. In this article, we demonstrate this through the proposal of a methodology that tackles both optimization of maintenance and estimation of failure risks at the same time. The methodology takes as input the remaining useful life of the system at instant t and determines the optimal inspection step and the threshold of remaining useful life for predictive maintenance. The originality of the methodology consists of using a theory inspired by behavioral economics called prospect theory. Prospect theory allows modeling the outcome of a decision making by considering several aspects related to the decision maker, mainly loss aversion and a tendency to overestimate events with low probability of occurrence but with high economic losses. A case study was then developed where both cases were considered: with prospect theory and without prospect theory. A sensitivity analysis of the results under variation of some input parameters was carried out in a final step to confirm the consistency of the results and show the interest of prospect theory.

Application of Prospect Theory in Obstetrics by Evaluating Mode of Delivery and Outcomes in Neonates Born Small or Appropriate for Gestational Age

(Abstracted from JAMA Network Open 2022;5(3):e222177) The antenatal diagnosis of fetal weight is a challenge that relies on sonographic estimations and the clinical assessment of fetal and maternal status. Meanwhile, the rate of detection of fetal growth restriction (FGR) is <40%.

Application of Prospect Theory in Obstetrics by Evaluating Mode of Delivery and Outcomes in Neonates Born Small or Appropriate for Gestational Age

Antenatal diagnosis of fetal weight is challenging, and the detection rate of fetal growth restriction (FGR) is low. Neonates with FGR are known to have an increased rate of obstetric intervention during labor, but the association of antenatal fetal weight estimation with mode of delivery and neonatal outcomes among neonates who are small and appropriate for gestational age (SGA and AGA) has not been reported. To evaluate the association of antenatal fetal weight estimation with mode of delivery and neonatal outcomes among neonates who are SGA and AGA, applying psychological concepts of cognitive bias and prospect theory to a model of clinical behavior. This cohort study was conducted between 2019 and 2020 using data from 2006 to 2018 at a tertiary care center in Jerusalem, Israel. Participants were 100 198 term singleton neonates without anomalies who were categorized into 4 groups according to the presence of an antenatal suspicion of FGR and final birth weight. Neonates with false positives (FPs; ie, group 1-FP: those with suspected FGR who were AGA) and neonates with true positives (TPs; ie, group 2-TP: those with suspected FGR who were SGA) were compared with neonates with AGA antenatal fetal weight estimation, including neonates with false negatives (FNs; ie, group 3-FN: those not suspected to have FGR who were SGA) and neonates with true negatives (TNs; ie, group 4-TN: those not suspected to have FGR who were AGA). Data were analyzed from July 2019 to July 2020. Fetal weight estimation was performed according to sonographic and clinical evaluation at admission to labor, with FGR defined as a birth weight less than the 10th percentile for gestational age. Sonographic fetal weight estimation was performed according to Hadlock formula. Clinical weight estimation was performed by trained obstetricians. The primary outcomes were obstetric intervention and mode of delivery; the secondary outcomes were neonatal Apgar score (with low Apgar score defined as <7) and neonatal intensive care unit (NICU) admission rates. Among 100 198 neonates eligible for the study (50941 [50.8%] male neonates), there were 5671 neonates in group 1-FP, 3040 neonates in group 2-TP, 8508 neonates in group 3-FN, and 82 979 neonates in group 4-TN. Mean (SD) maternal age was 28.6 (5.7) years. Among 8711 neonates with suspected FGR, 34.9% were below the 10th percentile at birth, while 65.1% were AGA. Neonates with suspected FGR had a significantly increased rate of induction of labor (group 1-FP: 649 neonates [11.4%] and group 2-TP: 969 neonates [31.9%]) compared with neonates in group 3-FN (1055 neonates [12.4%]) and group 4-TN (7136 neonates [8.6%]) (P < .001) and a significantly increased rate of cesarean delivery (group 1-FP: 915 neonates [16.1%] and group 2-TP: 556 neonates [18.3%] vs group 3-FN: 1106 neonates [13.0%] and group 4-TN: 6588 neonates [7.9%]; P < .001). Increased NICU admission was found for neonates who were SGA compared with neonates who were AGA (group 2-TP: 182 neonates [6.0%] and group 3-FN: 328 neonates [3.9%] vs group 1-FP: 51 neonates [0.9%] and group 4-TN: 704 neonates [0.8%]; P <.001), as was increased rate of low Apgar score (eg, at 1 minute: group 2-TP: 149 neonates [4.9%] and group 3-FN: 384 neonates [4.5%] vs group 1-FP: 124 neonates [2.2%] and group 4-TN: 1595 neonates [1.9%]; P < .001). In a multivariable model comparing group 1-FP, group 2-TP, and group 3-FN with group 4-TN, suspicion of FGR was independently associated with increased risk of caesarean delivery among neonates in group 1-FP (odds ratio, 1.72; 95% CI, 1.56-1.88; P < .001). This study found that antenatal diagnosis of FGR was independently associated with an increase in risk of caesarean delivery by 70% in neonates who were AGA without improvement in neonatal outcomes. These findings suggest that such outcomes may be explained by application of prospect theory and may be associated with cognitive bias in clinical decision-making.

Public health emergency decision-making and management system sound research using rough set attribute reduction and blockchain

Public health emergency decisions are explored to ensure the emergency response measures in an environment where various emergencies occur frequently. An emergency decision is essentially a multi-criteria risk decision-making problem. The feasibility of applying prospect theory to emergency decisions is analyzed, and how psychological behaviors of decision-makers impact decision-making results are quantified. On this basis, the cognitive process of public health emergencies is investigated based on the rough set theory. A Decision Rule Extraction Algorithm (denoted as A-DRE) that considers attribute costs is proposed, which is then applied for attribute reduction and rule extraction on emergency datasets. In this way, decision-makers can obtain reduced decision table attributes quickly. Considering that emergency decisions require the participation of multiple departments, a framework is constructed to solve multi-department emergency decisions. The technical characteristics of the blockchain are in line with the requirements of decentralization and multi-party participation in emergency management. The core framework of the public health emergency management system-plan, legal system, mechanism, and system can play an important role. When delta { = }0.10, the classification accuracy under the K-Nearest Neighbor (KNN) classifier reaches 73.5%. When delta { = }0.15, the classification accuracy under the Support Vector Machines (SVM) classifier reaches 86.4%. It can effectively improve China’s public health emergency management system and improve the efficiency of emergency management. By taking Coronavirus Disease 2019 (COVID-19) as an example, the weight and prospect value functions of different decision-maker attributes are constructed based on prospect theory. The optimal rescue plan is finally determined. A-DRE can consider the cost of each attribute in the decision table and the ability to classify it correctly; moreover, it can reduce the attributes and extract the rules on the COVID-19 dataset, suitable for decision-makers' situation face once an emergency occurs. The emergency decision approach based on rough set attribute reduction and prospect theory can acquire practical decision-making rules while considering the different risk preferences of decision-makers facing different decision-making results, which is significant for the rapid development of public health emergency assistance and disaster relief.

Simulation Analysis of the Evolution of Sustainable Operation of Transport Infrastructure Projects under Government Regulation Based on Prospect Theory and BP Neural Network

The decisions and actions of operators in the operation of transport infrastructure play a crucial role in the sustainability of the project but are influenced by factors such as the strength of government regulation and the frequency of use by users. The influence of social recognition and acceptance by the authorities on the decisions of the various parties involved in a project is becoming increasingly significant. To address this issue, this paper attempts to apply prospect theory to characterize the impact of changes in recognition on the decisions of project managers and the government from the perspective of recognition and to construct a tripartite evolutionary game model for the government, the operator, and the users, taking into account the combined effect of multiple factors, to explore the evolutionary law of the operator’s strategy choice. Evolutionary game theory, in which each person is considered irrational and behavior is changeable, is more realistic. The addition of prospect theory allows the model to more realistically reflect the decisions of each participant in the game process when faced with risk. The results of the study show that there is an optimal level of external regulation to maximize the benefits for all three parties in the game, strong government regulation does not necessarily improve service quality, operators tend to provide low-quality services in the game process and incentives should be increased, and that attempts should be made to provide users with a variety of transport infrastructure options to ensure that users’ interests are maximized. The paper further analyzes the indicators established by sensitive factors using BP neural networks on the basis of the analysis of transportation infrastructure operation and impact sensitive factors using evolutionary games and finds that the conclusions obtained by deep learning algorithms are more consistent with those obtained using evolutionary games, achieving cross-validation of the results. The reliability of the result is improved, and it is demonstrated that deep learning algorithms can be introduced as a supplement in the process of future analysis of transportation infrastructure operations. Finally, management suggestions are made in light of the actual situation.

How pandemic severity moderates digital food ordering risks during COVID-19: An application of prospect theory and risk perception framework

COVID-19 presents both challenges and opportunities to the hospitality industry. The purpose of this study is to examine the effects of perceived risk on intention to order digital food deliveries during the pandemic. Drawing upon the prospect theory and risk perception framework, the study proposed and tested an integrated model of perceived risk with a moderating role of pandemic severity. The results prove the negative impact of perceived risk on purchase intention and identify company's competency as an antecedent of perceived risk. However, consumers' knowledge regarding COVID-19 fails to predict their perceived risk. Different moderating effects are revealed based on pandemic severity. The effect of perceived risk on purchase intention is moderated by self-efficacy in severe pandemic regions, whereas by risk propensity in mild pandemic regions. The findings offer empirical evidence and managerial suggestions to assist hospitality businesses' crisis marketing and risk management.

전망이론 관점에서 기술수용모델의 해석에 관한 연구 - 음성 AI 비서 서비스 수용자와 비수용자의 비교 중심으로 -

전망이론 관점에서 기술수용모델의 해석에 관한 연구 - 음성 AI 비서 서비스 수용자와 비수용자의 비교 중심으로 -

Review of route choice based on prospect theory

In the face of the traffic system with uncertainty, people often take on the characteristics of the bounded rationality, there is a certain degree of uncertainty, the application of prospect theory has some applicability to the modeling of route choice decision behavior. This paper reviews the development course of prospect theory, then expounds the formula principle of prospect theory and the improved cumulative prospect theory, introduces the research status at home and abroad and makes a certain comb and summary.

Applying prospect theory to pilot weather‐related decision‐making: The impact of monetary and time considerations on risk taking behaviour

SummaryOne of the key findings of prospect theory is that people tend to treat potential gains differently to potential losses. Consistent with earlier findings across a range of areas, pilots were risk averse when faced with an uncertain situation involving monetary gains and risk seeking when faced with a monetary loss. Prospect theory has largely been used to explore monetary decision‐making; however, “time” is potentially a more important consideration for pilots than money. For example, how much time can be flown with the current fuel onboard. It was found that pilots' decision behaviour changed when faced with a decision involving time, with pilots risk averse for both a time gain and a time loss situation. Pilots appeared to prefer to know precisely the time required for a journey, rather than take a gamble on a potential short cut. Evidence also suggests pilots were more likely to take risks in situations that they perceive they have more control over (e.g. air traffic delays) compared to dynamic weather‐related events. There was some evidence to suggest pilots do not consider a decision in terms of an end state, but rather in terms of losses and gains from their current state. The final part of the study found evidence that it may be possible to predict pilot risk taking behaviour using self‐report decision frames.

Risk preferences of commodity crop producers and specialty crop producers: An application of prospect theory

AbstractProducers’ decisions, such as crop insurance, contract agreement, and technology adoption, involve considerable risk and uncertainty. Particularly, specialty crop production is more vulnerable to risk and requires more intensive management than commodity crop production, while risk mitigation tools for specialty crop production are comparatively limited. We apply Prospect Theory (PT) to analyze risk preferences of U.S. producers, and further compare the preference differences between commodity crop and specialty crop producers. Reference dependent, diminishing sensitivity, loss aversion, and probability weighting, as well as certain farm characteristics and producer demographics, are found to have a significant impact on grower risk attitudes. In addition, we do not observe significant differences in the base PT estimates between commodity crop and specialty crop producers. However, the relationships between risk behavior and individual characteristics vary between the two types of producers, which shed lights on the development of agricultural policies and provide implications for the design of contract and insurance.

Do Accounting and Finance Master’s Students Apply Prospect Theory?

This study aims to question the assumptions of prospect theory using a sample of students enrolled in a master’s course on accounting and finance at a Portuguese polytechnic institution. Such theory has stood out among others developed in the field of Behavioural Finance due to the debate and investigation it has generated. To achieve this aim, we applied a questionnaire four consecutive years (2012–2015). The instrument included a set of alternative response questions that seek to unveil respondents’ preferences regarding the situations they were presented with. Bibliographic and descriptive research was carried out and the results were compared with those obtained by other authors but they were not always consistent. Thus, the isolation effect was confirmed; the reflection effect was almost always confirmed; and the certainty effect was not always confirmed. Regarding attitude toward risk, the assumptions of risk aversion, and importance given to changes in wealth (at the expense of wealth states), our results are in line with those obtained by said authors. Hence, this study contributes to support prospect theory with its results and the confirmation of the isolation effect.

Risk-Based Multi-Attribute Decision-Making for Normal Cloud Model Considering Pre-Evaluation Information

An uncertain multi-attribute decision-making (MADM) problem is studied based on cloud models. Cloud models, referring to fuzziness and randomness, are utilized to depict evaluation and pre-evaluation information which can reflect the future development performance of alternatives. Because of bounded rationality, decision maker's (DM) risk attitudes should be considered when facing uncertainty. Thus, a behavioral MADM (BMADM) method is proposed by considering DM's risk attitudes and pre-evaluation. First, a distance measure for normal cloud models is developed with consideration of both DM's risk preferences and random distribution, aiming at making full use of information. Second, as a basis of applying prospect theory, positive ideal reference point is set by considering both evaluation and pre-evaluation information from three aspects: risk-averse, risk-neutral, and risk-seeking preference coefficients, in which the sign of distance is not necessary to determine. The third element is the establishment of an optimization model for handling incomplete attribute weights, following which is to obtain the ranking of alternatives. The final phase is the application of the proposed method to one case, along with sensitivity and comparison analyses, as a means of illustrating the applicability and feasibility of the new method.

Compensating Qualitative Rating Distortion of User Experience Evaluation Based on Prospect Theory

Psychophysical assessment may be affected by cognitive distortion. Although the theory was originally developed to revise decision making in uncertain situations, prospect theory can be applied to psychophysical measurements, which was verified in a previous preliminary study. Two case studies were used to validate the utilization of prospect theory in psychophysical measurements. Affective satisfaction dimensions were rated by participants for an experimental device using a 0–100 scale. Performance of affective satisfaction models increased with the application of prospect theory-based compensation. Hundreds of participants evaluated the user value of their own devices via an online questionnaire. Although model fit performance increased slightly with transformed data, more case studies are needed to investigate the utility of prospect theory on user value or on a range of target constructs. The application of prospect theory in various situations of psychophysical measurement can be expected to improve and compensate for measurement results.

Applying Prospect Theory to Participation in a CAPI/Web Panel Survey.

Prospect theory states that the influential power of avoiding negative outcomes is stronger than that of achieving positive outcomes. In a survey context, this theory has been tested with respect to not only participation in a CATI survey, but also giving consent to data linkage in CATI surveys. No study, however, has tested the theory with respect to participation in a CAPI or web survey. This study does so in a mixed-mode panel context; it also tests the moderating effects of time-in-panel, response history, and mode protocol. Results show that the framing of the survey participation request influences participation propensity in a way consistent with prospect theory, but only for relatively recent panel entrants. The opposite effect is found for long-term panel participants. No difference is found between mode protocols.

Selling the Iran Nuclear Agreement: Prospect Theory and the Campaign to Frame the Joint Comprehensive Plan of Action

This paper examines the Obama administration’s arguments for ratifying the Joint Comprehensive Plan of Action (JCPOA) to limit Iran’s nuclear capabilities and whether these claims encouraged U.S. senators to approve the agreement. The analysis relies on Kahneman and Tversky’s (1979) prospect theory in which more risk is taken to avoid certain loss and improve conditions when options are framed in terms of losses rather than gains. We argue that the Obama administration was able to persuade Democratic senators to support the JCPOA by arguing the agreement had the potential to curb the development of nuclear weapons by the Iranian regime. But more importantly, the administration was able to frame the status quo as a certain loss, and thus, the risk associated with the JCPOA was acceptable in comparison to not adopting the agreement. Our analysis of the Obama administration’s lobbying efforts and the Democratic senators' statements in support of the agreement demonstrate the value of applying prospect theory to understanding decision making with respect to American foreign policy formulation.

Applied prospect theory: assessing the βs of M&amp;amp;A-intensive firms

Behavioral components of Kahneman and Tversky’s (1979) prospect theory (PT) were applied to derive an adjusted Capital Asset Pricing Model (CAPM) in the estimation of merger and acquisition-intensive firms’ expected returns. The premise was that the CAPM – rooted in expected utility theory – is violated by the behavioral biases identified in prospect theory. Kahneman and Tversky’s prospect theory (1979) has demonstrated that weaknesses abound in the viability of classical utility theory predictions. For mergers and acquisitions, firms appear to be isolated from and immune to human error, yet decisions which involve the undertaking of capital-intensive projects are delegated to senior management. These individuals are prone to cognitive biases and personalized risk appetites that may (and often do) compromize attitudes and behavior when it comes to pricing risky ventures. Having established that beta estimates using linear regression are inferior, the CAPM was implemented utilizing beta estimates obtained from the Kalman filter. The results obtained were assessed for their long-term market price predictive accuracy. The authors test the reliability of the CAPM as a predictor of price, observe the rationality of human behavior in capital markets, and attempt to model premiums to adjust CAPM returns to a level that more appropriately accounts for firm specific risk. The researchers show that market participants behave irrationally when assessing M&amp;amp;amp;A firms’ specific risk. Logistic regression coupled with the development of a risk premium was implemented to correct the original Kalman filter returns and was tested for improvements in predictive power.