There is a disconnect in the literature between analyses of risky choice based on cumulative prospect theory (CPT) and work on predecisional information processing. One likely reason is that for ...expectation models (e.g., CPT), it is often assumed that people behaved only as if they conducted the computations leading to the predicted choice and that the models are thus mute regarding information processing. We suggest that key psychological constructs in CPT, such as loss aversion and outcome and probability sensitivity, can be interpreted in terms of attention allocation. In two experiments, we tested hypotheses about specific links between CPT parameters and attentional regularities. Experiment 1 used process tracing to monitor participants' predecisional attention allocation to outcome and probability information. As hypothesized, individual differences in CPT's loss-aversion, outcome-sensitivity, and probability-sensitivity parameters (estimated from participants' choices) were systematically associated with individual differences in attention allocation to outcome and probability information. For instance, loss aversion was associated with the relative attention allocated to loss and gain outcomes, and a more strongly curved weighting function was associated with less attention allocated to probabilities. Experiment 2 manipulated participants' attention to losses or gains, causing systematic differences in CPT's loss-aversion parameter. This result indicates that attention allocation can to some extent cause choice regularities that are captured by CPT. Our findings demonstrate an as-if model's capacity to reflect characteristics of information processing. We suggest that the observed CPT-attention links can be harnessed to inform the development of process models of risky choice.
We develop a conceptual model of entrepreneurial exit which includes exit through liquidation and firm sale for both firms in financial distress and firms performing well. This represents four ...distinct exit routes. In developing the model, we complement the prevailing theoretical framework of exit as a utility-maximizing problem among entrepreneurs with prospect theory and its recent applications in liquidation of investment decisions. We empirically test the model using two Swedish databases which follow 1,735 new ventures and their founders over eight years. We find that entrepreneurs exit from both firms in financial distress and firms performing well. In addition, commonly examined human capital factors (entrepreneurial experience, age, education) and failure-avoidance strategies (outside job, reinvestment) differ substantially across the four exit routes, explaining some of the discrepancies in earlier studies.
A new model of utility analysis and performance prediction in crowdfunding is proposed in this paper with considering backer’s behavior-related decision. To establish a connection between the ...backer’s behavior-related decision and crowdfunding performance, the proposed model includes a modeling analysis and an empirical analysis. The modeling analysis focuses on analyzing backer’s backing decision with establishing the utility-driven model, while the empirical analysis verifies the association between the utility-related features and crowdfunding performance, as well as the prediction improvement of the proposed model. Specifically, prospect theory (PT) is applied first in the modeling analysis part to analyze backer’s prospect utilities, and then evidential theory (ET) is applied to aggregate the multi-source dynamic feature’s utility. To validate the proposed model, and based on the results of modeling analysis, we conduct the significant test with regression model and the prediction test with several machine-learning prediction model to verify whether the proposed utility features can improve the prediction results in terms of crowdfunding performance. The empirical results show that the correlation between behavior-related factors and crowdfunding performance is significant. And the area under the curve (AUC) index of the proposed model is, on average, 7.39% higher than the baseline model on several machine-learning prediction models. In addition to providing new insights for predicting crowdfunding results, this research provides a powerful tool to assist crowdfunding platform and fundraisers in operational management with analyzing backers’ backing behavior.
•Modeling backers’ behavior-related decisions in crowdfunding.•Empirical results validate the effect of behavior-related features on performance.•On average, the proposed model improves prediction accuracy by 7.39%.
Unlike most existing clustering methods, data envelopment analysis (DEA) clusters decision-making units (DMUs) based on production characteristics rather than distance. The clustering results ...obtained using the DEA clustering approach reflect the production relationship between the inputs and outputs of the DMUs to better identify the inherent production correlation between them. However, existing DEA-based clustering approaches struggle to rationally assign unique clusters to DMUs that exhibit multiple production characteristics and lack the further processing of clustering results. Therefore, this study proposes a new DEA clustering approach based on the individual perspective of DMUs that incorporates prospect theory to reflect the individual preferences of DMUs to assign each DMU to a relatively unique cluster. Furthermore, a clustering adjustment method and a clustering reduction method are proposed to further improve the clustering quality. The former can handle some special clusters according to the decision-maker’s preference, and the latter permits the realization of an arbitrary number of clusters. The new DEA clustering approach is more reliable and flexible, and more valuable information can be provided for decision-makers. Finally, the validity of the new approach is verified through a comparison with existing approaches in two numerical cases, and an empirical example is used to illustrate its practicability.
Dealing nuclear wastewater into the sea significantly threatens the global ecological environment and public health. Existing research primarily focuses on the roles of emission and regulatory ...bodies, often overlooking the collaborative interactions between these entities. This study extends traditional marine governance strategies by considering upstream and downstream countries as research subjects. We first establish an evolutionary game model for the collaborative governance of nuclear wastewater between upstream and downstream countries, utilizing the Prospect theory and Evolutionary Game Theory. Subsequently, numerical simulations are employed to explore different entities' decision-making behaviors and influencing factors. Our findings indicate that: (1) initial willingness affects the time it takes for the system to reach a stable state; (2) the benefits and costs of proactive governance strategies influence decision-maker's behaviors; (3) subjective factors (risk preference, loss aversion) affect the strategic choices. This research provides a scientific basis for understanding different countries' selection of nuclear wastewater governance strategies.
Urgent and critical situations or so-called emergency events, such as terrorist attacks and natural disasters, often require crucial decisions. When an emergency event occurs, emergency decision ...making plays an important role in dealing with it, and hence, its importance nowadays is increasing. In the real world, it is difficult for only one decision maker to take a comprehensive decision for coping with an emergency event. Consequently, many practical emergency problems are often characterized by a group emergency decision making (GEDM) scheme. Different studies show that human beings are usually bounded rational under risk and uncertainty, and their psychological behavior is very important in the decision-making process. However, such behavior is neglected in current GEDM studies. Therefore, this study proposes a novel GEDM method that considers experts’ psychological behavior in the GEDM process. The method is then applied to a case study and compared with other related approaches. Finally, discussions are presented to illustrate the novelty, feasibility, and validity of the proposed GEDM method, showing the importance of experts’ psychological behavior in GEDM.
The fourth industrial revolution, also labelled Industry 4.0, was beget with emergent and disruptive intelligence and information technologies. These new technologies are enabling ever-higher levels ...of production efficiencies. They also have the potential to dramatically influence social and environmental sustainable development. Organizations need to consider Industry 4.0 technologies contribution to sustainability. Sufficient guidance, in this respect, is lacking in the scholarly or practitioner literature. In this study, we further examine Industry 4.0 technologies in terms of application and sustainability implications. We introduce a measures framework for sustainability based on the United Nations Sustainable Development Goals; incorporating various economic, environmental and social attributes. We also develop a hybrid multi-situation decision method integrating hesitant fuzzy set, cumulative prospect theory and VIKOR. This method can effectively evaluate Industry 4.0 technologies based on their sustainable performance and application. We apply the method using secondary case information from a report of the World Economic Forum. The results show that mobile technology has the greatest impact on sustainability in all industries, and nanotechnology, mobile technology, simulation and drones have the highest impact on sustainability in the automotive, electronics, food and beverage, and textile, apparel and footwear industries, respectively. Our recommendation is to take advantage of Industry 4.0 technology adoption to improve sustainability impact but each technology needs to be carefully evaluated as specific technology will variably influence industry and sustainability dimensions. Investment in such technologies should consider appropriate priority investment and championing.
Emergency response of a disaster is generally a risk decision-making problem with multiple states. In emergency response analysis, it is necessary to consider decision-maker's (DM's) psychological ...behavior such as reference dependence, loss aversion and judgmental distortion, whereas DM's behavior is neglected in the existing studies on emergency response. In this paper, a risk decision analysis method based on cumulative prospect theory (CPT) is proposed to solve the risk decision-making problem in emergency response. The formulation and solution procedure of the studied emergency response problem are given. Then, according to CPT, the values of potential response results concerning each criterion are calculated. Consider the interdependence or conflict among criteria, Choquet integral is used to determine the values of each potential response result. Accordingly, the weights of probabilities of all potential response results are calculated. Furthermore, by aggregating the values and weights of response results, the prospect value of each response action (alternative) is determined, and overall prospect value of each response action is obtained by aggregating the prospect value and the cost of each action. According to the obtained overall prospect values, a ranking of all response actions can be determined. Finally, based on the background of emergency evacuation from barrier lake downstream villages, an example is given to illustrate the feasibility and validity of the proposed method.
The canonical conclusion from research on age differences in risky choice is that older adults are more risk averse than younger adults, at least in choices involving gains. Most of the evidence for ...this conclusion derives from studies that used a specific type of choice problem: choices between a safe and a risky option. However, safe and risky options differ not only in the degree of risk but also in the amount of information to be processed-that is, in their complexity. In both an online and a lab experiment, we demonstrate that differences in option complexity can be a key driver of age differences in risk attitude. When the complexity of the safe option is increased, older adults no longer seem more risk averse than younger adults (in gains). Using computational modeling, we test mechanisms that potentially underlie the effect of option complexity. The results show that participants are not simply averse to complexity, and that increasing the complexity of safe options does more than simply make responses more noisy. Rather, differences in option complexity affect the processing of attribute information: whereas the availability of a simple safe option is associated with the distortion of probability weighting and lower outcome sensitivity, these effects are attenuated when both options are more similar in complexity. We also dissociate these effects of option complexity from an effect of certainty. Our findings may also have implications for age differences in other decision phenomena (e.g., framing effect, loss aversion, immediacy effect).
This study examines the predictive power of performance persistence from the perspective of prospect theory using 12 months of return distributions. Performance persistence stems from unique ...information that differs from momentum, disposition effect, and firm-specific variables related to the trading behaviors of individual investors. The novel cross-sectional prospect theory value (CSPTV) measurement, designed to reflect cross-sectional comparisons across return distributions for all stocks, captures this unique information better than the existing prospect theory value (PTV), which is specific to a single stock. In other words, CSPTV improves the predictive power of prospect theory by providing performance with a larger magnitude and greater significance than PTV. Consequently, this study anticipates differentiated contributions from the CSPTV design, which expands the application scope of the existing prospect theory to the past 12-month return distribution and improves the predictive power of prospect theory in cross-sectional stock returns.
•We expand the application of the prospect theory value (PTV) to the past 12-month return distribution•The predictive power of existing PTV using 12-month return distributions is confirmed.•Significant positive performance stems from unique information distinct from momentum.•We devised a novel method of cross-sectional prospect theory value (CSPTV).•The CSPTV improves the predictive power of prospect theory compared to that of existing PTV.
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