Can redemption option innovation alleviate the constraints of risk perception on demand willingness for housing reverse mortgages in China?

A human-Like interactive driving decision-making model integrating cumulative prospect theory

Lotteries are a prevalent form of gambling between a seller and buyers. Designing a lottery requires a model of how buyers make decisions when confronted with uncertain outcomes. Cumulative prospect theory (CPT) is a descriptive model that captures people’s propensity to overestimate extreme events and their different attitudes toward gains and losses. In this study, we design a lottery that maximizes the seller’s profit when the buyers’ decision-making adheres to the CPT framework. The main difficulty is the nonconvexity of the CPT framework, which we overcome by reformulating the problem as a three-level optimization problem and characterizing its optimal solution. Based on the analysis, we propose a linear-time algorithm that computes the optimal lottery. Furthermore, we present an efficient algorithm applicable to a broader setting with a ticket price constraint. This is the first study to employ the CPT framework in designing an optimal lottery with more than two outcomes.

Abstract This study examines whether the inclusion of cryptoassets improves optimal portfolio performance for key investor archetypes in behavioral finance: Cumulative Prospect Theory (CPT), Markowitz, and Loss Averse investors. We develop a framework that integrates Second-Order Stochastic Dominance (SSD) with Stochastic Spanning to construct optimal portfolios, accounting for investors’ subjective risk perceptions and narrow framing. Our methodology incorporates non-stationarities, asset return bubbles, and the safe-haven role of gold while also considering the impact of the COVID-19 pandemic. Empirical results, based on a rolling-window analysis of business-day returns, indicate that while the traditional portfolio universe (stocks, bonds, and gold) generally spans the augmented universe (including cryptoassets) in-sample, out-of-sample tests reveal that cryptoassets enhance performance, particularly for CPT and Loss Averse investors. The findings suggest that behavioral criteria do not overturn the fundamental instability and risk characteristics of crypto assets, although under certain behavioral biases, investor risk perceptions shape portfolio choices in ways that cryptoassets can appear as attractive investment options.

Risk assessment of salt cavern hydrogen storage projects based on spherical fuzzy sets and cumulative prospect theory - TOPSIS

A quantum group decision-making model for patient-capital project selection integrating cumulative prospect theory under linear Diophantine fuzzy uncertainty

ABSTRACT Understanding bus commuters’ departure time choices and perceived waiting time value under lateness anxiety is critical for modeling perceived travel costs and optimizing bus operations. Work arrival constraints induce anxiety that alters waiting time valuation and may trigger mode switching, while uncertainty-driven bounded rationality further complicates decision-making. This paper proposes a Cumulative Prospect Theory (CPT)-based Logit model incorporating work-arrival-time-related decision thresholds. The model captures threshold-dependent waiting time valuation, threshold-triggered mode switching, and lateness penalties with threshold-graded probability, thereby quantifying how anxiety shapes utility values across different departure times. Using Stated Preference survey data, we estimate time-dependent waiting time values and CPT parameters. Results show that the perceived waiting time value increases substantially as the work arrival time approaches, rising from CNY 27.93/hour before the anxiety start time to CNY 75.00/hour after the latest feasible time for taking the bus. The findings provide behavioral insights for bus operations and commuter planning.

This paper proposes a multi-attribute decision-making method integrating expert weighting and psychological preferences to enhance bridge strengthening decisions. Using Pythagorean fuzzy sets (PFS), an expert evaluation matrix is constructed. Expert weights are determined by fusing subjective and objective factors ( λ ∈ [ 0 , 1 ] , γ = 0.5 ), while the CRITIC method calculates indicator weights ( ω j ∈ [ 0 , 1 ] ). Cumulative prospect theory (CPT) is introduced to capture risk attitudes, and the VIKOR method is applied for alternative ranking. Based on the established evaluation index system for bridge strengthening schemes (Z = 15), a case study of a bridge with four strengthening alternatives (A1–A4) evaluated by five experts shows the following results: group utility values S i are 0.656, 0.659, 0.484, and 0.225; individual regret values RI i are 0.109, 0.093, 0.078, and 0.071; compromise indices Q are 0.000, 0.3947, 0.7946, and 0.9972, identifying A4 as optimal. Sensitivity analysis via 50 Monte Carlo simulations confirms ranking stability under variations in CPT parameters ( α , β , σ , ε + , ε − ) and γ ∈ [ 0 , 1 ] , with non-overlapping 95% CIs. Comparative analysis aligns with existing methods, demonstrating the PFS–CPT–VIKOR framework’s scientific validity and applicability.

ABSTRACT Industry 5.0 advances a human‑centric paradigm where human – automation symbiosis (HAS) depends on aligning operator cognition with automated precision. This paper proposes manufacturing nudging as an engineering mechanism to steer operator behavior while preserving autonomy and develops a complete pipeline for nudge design and personalization at production scale. First, a conjoint prospect‑theoretic model captures dual stakeholder valuations – operators (workload, burden) and managers (quality, cycle time) – and aggregates multiple nudging features via cumulative prospect theory to reflect realistic risk, loss‑aversion, and probability‑weighting effects. Second, hierarchical Bayesian parameterization supports customization (segment‑level) and personalization (operator‑level) with coherent prior constraints and MCMC‑based estimation. Third, an engineering‑cost model estimates operation cycle time with a neural network trained on operator – nudge – scenario data. Finally, a two‑dimensional genetic algorithm (2D‑GA) optimizes operator‑to‑nudge assignments, exploiting a matrix encoding that preserves operator × feature structure and thereby circumvents the sparsity and locality issues of 1D encodings. A jet‑engine assembly case study with AR‑based nudges demonstrates increased symbiotic value with controlled cycle‑time cost; optimizing purely for behavioral value yields only a 0.15% gain but a 3.33% higher cost, whereas the multi‑objective formulation attains near‑maximal value at substantially lower cost. The results establish a rigorous, scalable, and interpretable approach to nudging for HAS in complex

<div>Path selection for the transport of hazardous materials (Hazmats) is a multi-facet decision problem that needs to account for multiple factors such as accident risk as well as transportation cost. Most existing literature has modeled the risk of Hazmats transportation as the product of accident loss, and its probability-based expected utility theory, however, could be problematic since such a risk definition does not necessarily reflect the real perceived risk by the decision-maker. This article proposes a novel approach to the path selection of Hazmats transportation based on the cumulative prospect theory (CPT). Specific steps in the decision of path selection are first laid out in the framework of CPT. Value (Loss) functions of accident in Hazmats transportation are then derived, together with the decision weighting function reflecting accident probabilities. For illustration, a case study is conducted using transportation data from a Hazmats transportation firm in Shanghai. Comparisons of path selections among the newly proposed approach, the existing methods based on expected utility theory, and the actual outcome from the decision-makers clearly indicate the superior performance of the proposed method. The results will enhance the safety level of road transportation of Hazmats.</div>

This research extends the current understanding of tourist loyalty for small island destinations. Using cumulative prospect theory, we establish and test research models to examine the such underlying determinants as motivation, the theory of planned behavior, image, and risk factors on tourist loyalty. Through the adoption of necessity and sufficiency logic, this research successfully identifies four exclusive conditions for tourist loyalty: necessary and sufficient, necessary but insufficient, unnecessary but sufficient, and unnecessary and insufficient. Subjective norm and affective image were found to be critical aspects required for the occurrence and enhancement of tourist loyalty. Additionally, through case-based analysis, the study reveals that multiple necessary conditions and complex combinations of the above antecedents can also impact tourist loyalty, emphasizing the intricate nature of the factors influencing tourist loyalty in small island destinations. Theoretical significance and practical implications are discussed.

• This study utilizes cumulative prospect theory to evaluate city infrastructure development. • It views the difference between current and previous indicator performance as gains or losses. • A case study of 33 highly urbanized Philippine cities demonstrates the proposed approach. • Findings revealed that the proposed approach penalizes those with a plateaued or declining growth. • It captures temporal and perceptual development shifts, which better represent change dynamics. There is a broad consensus in the economic literature on the positive impact of infrastructure development on socio-economic indicators. Amplified by the estimated two-thirds of the population concentrating in cities by 2050, investments in infrastructure are required to support urban activities and connectivity. Accordingly, measuring the level of development in infrastructure, particularly in cities, becomes a pivotal agenda. However, the current literature highlights the use of the weighted sum method to aggregate relevant indicators into a composite index. While straightforward, the notion of linear utility fails to account for the psychological effects resulting from gains or losses in indicator performance inherent in human perception, which are exacerbated when used in time-series analysis. Thus, this work introduces a behavioral dynamic evaluation framework inspired by the cumulative prospect theory by (1) framing gains or losses of indicator performance, (2) introducing value and weighting functions for gains and losses, and (3) aggregating psychological utility across indicators to derive the rankings of cities. An actual case of evaluating the infrastructure development of 33 highly urbanized cities in the Philippines for 2015-2023, obtained from the Cities and Municipalities Competitiveness Index database, was implemented to demonstrate the proposed framework. Findings revealed the distinctiveness of rankings generated by the proposed framework, penalizing those cities that have plateaued or are experiencing declining growth. Also, it exhibits greater sensitivity, which could reflect external shocks. The proposed approach contributes to the literature supporting policy evaluation, equitable and behavioral planning, and investment targeting by capturing temporal and perceptual development shifts.

Summary A model-based workflow is presented for computable decision under uncertainty (DUU). The workflow can generate solutions to mitigate subsurface and operational uncertainties for transparent and robust decision-making. It consists of scenario generation, physics-based modeling, uncertainty assessment, economic analysis, and solution mining. A behavioral economics theory, cumulative prospect theory (CPT), is considered in the workflow to account for the effect of uncertainties on overall economics and decision. A decision tree is generated by mining the simulation results to inform decisions that make full sense in physics and in economics. The usefulness of this workflow is illustrated with an exemplary problem in steamflooding for heavy-oil recovery. The results show that the introduction of CPT for some cases may change the decision that was made on the basis of conventional expected utility theory (EUT).

Enhanced indexing using cumulative prospect theory utility function with expectile risk

Discretionary lane changing is one of the most important behaviors in vehicle dynamics. The analysis of discretionary lane changing can provide support for human-like autonomous driving system and microscopic traffic simulation. The rule based discretionary lane change decision-making model has the advantages of good interpretability and being in line with human intuitiveness, but the performance of the rule based models are relatively poor. The learning-based discretionary lane changing decision-making models perform better in lane changing analysis than that of the rule based models, while they require a large amount of training data and have poor interpretability. This study proposes a framework for discretionary lane changing decision-making based on cumulative prospect theory and decision tree model, and uses genetic algorithm to calibrate the parameters of the framework. The framework proposed in this study has good interpretability for discretionary lane changing behavior similar to rule-based models, and it can achieve good lane changing prediction performance. The Next Generation Simulation (NGSIM) dataset is used to validate the framework proposed in this study. The results show that the framework proposed in this study can achieve better performance of discretionary lane change prediction than the analysis model based on decision tree and the analysis model based on cumulative prospect theory. When predicting discretionary lane changing behavior 2–0 seconds in advance, the accuracy of the framework in the test set can reach 85.6%.

Traditional portfolio optimization approaches typically presume a completely rational market, overlooking investors' behavioral inclinations and the complexity of sparsity in high-dimensional data. To tackle these concerns, this study presents a novel multi-period sparse behavioral portfolio optimization model. In a multi-period context, the model incorporates a utility function based on cumulative prospect theory, effectively capturing the irrational behavioral characteristics of investors. By leveraging $ \ell_1 $-norm regularization, it attains portfolio sparsity in each period and minimizes turnover across periods. Next, we proposed a hybrid algorithm that integrates the alternating direction method of multipliers with the pooling-adjacent-violators algorithm to efficiently solve the newly formulated model. Furthermore, the framework incorporates environmental, social, and governance factors to evaluate their influence on investors' behavioral portfolios. Numerical experiments and empirical analyses demonstrated that the proposed method can efficiently solve the model, and the new model is capable of reducing risk and transaction costs.

How should well-being be prioritised in society, and what trade-offs are people willing to make between fairness and personal well-being? We investigate these questions using a stated preference experiment with a nationally quasi-representative UK sample (n = 300), in which participants evaluated life satisfaction outcomes for both themselves and others under conditions of uncertainty. Individual-level utility functions were estimated using an Expected Utility Maximisation (EUM) framework and tested for sensitivity to the overweighting of small probabilities, as characterised by Cumulative Prospect Theory (CPT). A majority of participants displayed concave (risk-averse) utility curves and showed stronger aversion to inequality in societal life satisfaction outcomes than to personal risk. These preferences were unrelated to political alignment, suggesting a shared normative stance on fairness in well-being that cuts across ideological boundaries. The results challenge use of average life satisfaction as a policy metric and support the development of nonlinear utility-based alternatives that more accurately reflect collective human values. Implications for public policy and well-being measurement are discussed.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11205-026-03854-4.

Abstract Autonomous vehicle (AV) lane-changing decision-making is a crucial component of intelligent driving systems, requiring a balance between traffic efficiency and driving safety. Utility-based methods are a well-known theory for AV decision-making in discretionary lane-changing scenarios. Traditional methods primarily rely on cost-benefit analysis but often fail to capture human-like decision patterns and behavioral diversity. To address these limitations, this study proposes a novel lane-changing decision-making model that integrates cumulative prospect theory (CPT) for interpretable human behavior prediction and social value orientation (SVO) to dynamically adjust the trade-off between efficiency and safety based on observed lane-changing times. Unlike conventional models that assign fixed weights to decision factors, our approach dynamically adjusts the trade-off between efficiency and safety based on the observed lane-changing durations. We utilize the highD dataset to ensure robust evaluation, with one subset for parameter identification and another subset for validation. Comparative experimental analysis demonstrates that our model significantly outperforms those existing utility-based methods and a decision-making model without behavior-prediction components, achieving higher accuracy (81.87%), F1-score (79.05% for lane-changing and 74.56% for lane-keeping), and G-mean (76.41%), particularly in lane-changing scenarios. These findings contribute to advancing AV lane-changing strategies, offering a more adaptive, human-like, and safety-conscious decision-making framework for real-world traffic environments.

ABSTRACT This study explored the factors influencing residents’ intentions to support Muslim-friendly tourism, focusing on attitudes, avoidance behaviors, image perceptions, knowledge, and experiences. The findings revealed that residents’ attitudes, avoidance responses, and perceived image of Muslim-friendly tourism significantly impacted their intentions, while knowledge and experiences did not have a significant predictive effect. By applying Cumulative Prospect Theory and utilizing fsQCA analysis, the study identified four key causal configurations that shape residents’ perceptions of Muslim-friendly tourism. It demonstrated that positive attitudes and avoidance responses are crucial factors. Notably, loss factors, such as Islamophobia, exerted a greater influence than gain factors, underscoring the need to address negative perceptions to enhance local acceptance of Muslim-friendly tourism. Additionally, a cross-cultural comparison indicated that US residents generally possess more positive perceptions, greater familiarity, and stronger support for Muslim-friendly tourism compared to South Korean residents, who tend to be more cautious and exhibit higher levels of avoidance.

As global population aging intensifies, elderly users are increasingly becoming an important focus in retail service design. However, most existing smart shopping carts fail to fully consider the behavioral characteristics, cognitive limitations, and operational demands of older adults, resulting in reduced usability and adoption rates. To address this issue, this study proposes a design evaluation method based on Cumulative Prospect Theory (CPT), aiming to integrate psychological mechanisms such as reference dependence, loss aversion, and probability weighting into product assessment. By combining the Analytic Hierarchy Process (AHP) with the entropy weight method, a hybrid multi-criteria evaluation framework is constructed to enhance both subjective rationality and data-driven objectivity. Expert scoring, user testing, and CPT-based evaluation were conducted to compare three elderly-oriented smart shopping cart design concepts, and the findings were further validated using the Post-Study System Usability Questionnaire (PSSUQ). Results indicate that the proposed method effectively reduces subjective bias, improves evaluation reliability, and provides practical guidance for optimizing age-friendly product design. This study therefore offers both theoretical insights and methodological support for advancing inclusive design practices.