Risk-averse Decision Research Articles

A risk-averse latency location-routing problem with stochastic travel times

In this paper, a latency location-routing problem with stochastic travel times is investigated. The problem is cast as a two-stage stochastic program. The ex-ante decision comprises the location of the depots. The ex-post decision regards the routing, which adapts to the observed travel times. A risk-averse decision-maker is assumed, which is conveyed by adopting the latency CVaRα as the objective function. The problem is formulated mathematically. An efficient multi-start variable neighborhood search algorithm is proposed for tackling the problem when uncertainty is captured by a finite set of scenarios. This procedure is then embedded into a sampling mechanism so that realistic instances of the problem can be tackled, namely when the travel times are represented by random vectors with an infinite support. An extensive computational analysis is conducted to assess the methodological developments proposed and the relevance of capturing uncertainty in the problem. Additional insights include the impact of the risk level in the solutions.

Risk-averse supply chain management via robust reinforcement learning

Classical reinforcement learning (RL) may suffer performance degradation when the environment deviates from training conditions, limiting its application in risk-averse supply chain management. This work explores using robust RL in supply chain operations to hedge against environment inconsistencies and changes. Two robust RL algorithms, Qˆ-learning and β-pessimistic Q-learning, are examined against conventional Q-learning and a baseline order-up-to inventory policy. Furthermore, this work extends RL applications from forward to closed-loop supply chains. Two case studies are conducted using a supply chain simulator developed with agent-based modeling. The results show that Q-learning can outperform the baseline policy under normal conditions, but notably degrades under environment deviations. By comparison, the robust RL models tend to make more conservative inventory decisions to avoid large shortage penalties. Specifically, fine-tuned β-pessimistic Q-learning can achieve good performance under normal conditions and maintain robustness against moderate environment inconsistencies, making it suitable for risk-averse decision-making.

Forces at play: A qualitative study of risk aversion, policy and decision making for children's physically active play in schools

This study investigated (i) the social and political factors driving risk averse decision making for children's physically active play in schools, and (ii) the policies shaping these decisions in Victoria, Australia. A theory-informed multi-method case study design combined policy document mapping, semi-structured interviews, and photo elicitation with 30 education policy actors. Data were analysed using reflexive thematic analysis. Findings showed the policy landscape was heavily skewed towards managing risk and safety (84%; n = 42), with fewer, mostly optional, policies for promoting physical activity, and a lack of policy to promote or protect play. Thematic findings indicated an intersecting range of factors in this setting (including occupational health and safety, knowledge and skill gaps, parent factors, children's physical capabilities, an undervaluing of play) can deter schools from providing an environment that enables children's freely chosen, physically active play. Multi-faceted, systems-based approaches that go beyond interventions aimed at individual behaviour change are needed.

Perception of Women Towards Investment in Different Investment Alternatives

Women are empowered to take active participation in investment activities through financial education and an inclusive investment environment. Several alternatives to investing are available, including mutual funds, real estate, precious metals, savings accounts, and stocks, which offer potential returns through regular income or capital appreciation. Investment alternatives are differentiated by their unique features, risks, returns, and ease of management. Investors, before investing, must take these features into account in relation to their risk tolerance, goals, and time horizon when considering any particular alternative. As women are recognized for making risk-averse investment decisions, their investments are impacted by numerous factors. The primary goal of this study is to understand the investment perception of women towards different investment alternatives. While the main factors influencing investment decisions are return, risk, and liquidity, several other factors specifically influence women investors, such as social and cultural norms, peers, and family members. Primary data was collected through a well-structured questionnaire consisting of Likert scale questions. SPSS software was used for data analysis. The initial results of the survey showed that 70% of the respondents had an average understanding of different investment options, whereas only 5% had high knowledge of different investment alternatives. When it comes to interest in a particular investment, mutual funds are the most preferred alternative. Additionally, 72% of women respondents show a moderate level of risk tolerance among all respondents in the study.

The Effect of Cultural Norms on Group Decision-Making in Japanese Corporations

This paper explores the impact of cultural norms, particularly collectivism and social harmony, on group decision-making practices in Japanese corporations. Focusing on consensus-building mechanisms such as nemawashi and ringi-sho, the study examines how these practices foster inclusivity, mitigate conflict, and promote long-term organizational stability. It also highlights the challenges posed by slow decision-making processes and the risk of groupthink, which can stifle innovation and agility in a fast-paced global market. While consensus-building leads to more widely supported and risk-averse decisions, the paper suggests that balancing these traditional practices with more agile decision-making models is essential for Japanese corporations to remain competitive. The findings provide valuable insights into how Japanese firms might integrate global business practices while preserving their cultural identity.

Training set optimization is a feasible alternative for perennial orphan crop domestication and germplasm management: an Acrocomia aculeata example.

Orphan perennial native species are gaining importance as sustainability in agriculture becomes crucial to mitigate climate change. Nevertheless, issues related to the undomesticated status and lack of improved germplasm impede the evolution of formal agricultural initiatives. Acrocomia aculeata - a neotropical palm with potential for oil production - is an example. Breeding efforts can aid the species to reach its full potential and increase market competitiveness. Here, we present genomic information and training set optimization as alternatives to boost orphan perennial native species breeding using Acrocomia aculeata as an example. Furthermore, we compared three SNP calling methods and, for the first time, presented the prediction accuracies of three yield-related traits. We collected data for two years from 201 wild individuals. These trees were genotyped, and three references were used for SNP calling: the oil palm genome, de novo sequencing, and the A. aculeata transcriptome. The traits analyzed were fruit dry mass (FDM), pulp dry mass (PDM), and pulp oil content (OC). We compared the predictive ability of GBLUP and BayesB models in cross- and real validation procedures. Afterwards, we tested several optimization criteria regarding consistency and the ability to provide the optimized training set that yielded less risk in both targeted and untargeted scenarios. Using the oil palm genome as a reference and GBLUP models had better results for the genomic prediction of FDM, OC, and PDM (prediction accuracies of 0.46, 0.45, and 0.39, respectively). Using the criteria PEV, r-score and core collection methodology provides risk-averse decisions. Training set optimization is an alternative to improve decision-making while leveraging genomic information as a cost-saving tool to accelerate plant domestication and breeding. The optimized training set can be used as a reference for the characterization of native species populations, aiding in decisions involving germplasm collection and construction of breeding populations.

Economic and risk based optimal energy management of a multi-carrier energy system with water electrolyzing and steam methane reform technologies for hydrogen production

This paper proposes stochastic programming to optimal economic scheduling problems of a sustainable integrated energy system with renewable resources including power, natural gas, and hydrogen carriers. Hydrogen is considered a byproduct of electrical power and natural gas carriers using water electrolysis and steam methane reform process technologies. The main objective is to minimize the operation and emission costs while addressing uncertainties of electric/heating/cooling demands, electricity price, and renewable generation using scenario-based stochastic programming. To achieve a risk-hedging strategy, downside risk constraints are involved to minimize the risky scenarios portfolio. The whole problem is modeled as mixed-integer linear programming in GAMS optimization software. The analysis revealed that the expected operation cost without DRC is $ 577.52 and the expected risk-in-cost value for risky scenarios is $ 470.7. However, a conservative decision for reaching zero risk is costly and increases the expected cost up to $ 2901.1, which is not economically viable. A modest decision-making strategy compromises the cost and risk values where for decreasing the risk-in-cost to $ 288.7 (36.8%), the expected cost increased by to $1010.39 (75.95%). Moreover, the risk-averse decision-maker lowers the power sold to the grid, increases the use of water electrolyzing instead of the SMR process to reduce the environmental charges, and tries to increase flexibility with the help of hydrogen and battery storage.

Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios

This paper considers a risk-averse Markov decision process (MDP) with non-risk constraints as a dynamic optimization framework to ensure robustness against unfavorable outcomes in high-stakes sequential decision-making situations such as disaster response. In this regard, strong duality is proved while making no assumptions on the problem’s convexity. This is necessary for some real-world issues, e.g., in the case of deprivation costs in the context of disaster relief, where convexity cannot be ensured. Our theoretical results imply that the problem can be exactly solved in a dual domain where it becomes convex. Based on our duality results, an augmented Lagrangian-based constraint handling mechanism is also developed for risk-averse reinforcement learning algorithms. The mechanism is proved to be theoretically convergent. Finally, we have also empirically established the convergence of the mechanism using a multi-stage disaster response relief allocation problem while using a fixed negative reward scheme as a benchmark.

A viable supplier selection with order allocation by considering robustness, risk-averse and blockchain technology

The research introduces a novel approach called Viable Supplier Selection with Order Allocation considering Robustness and Risk-Aversion (VSSOARR), incorporating Blockchain Technology (BCT). The model utilizes a Robust Stochastic Optimization (RSO) technique with Weighted Value at Risk (WVaR) in the cost function. The objective function of this model combines the expected value and WVaR cost. The BCT platform is recommended to enhance agility in the face of disruptions. The research proposes sustainable requirements such as minimizing CO2 emissions and energy consumption management, ensuring minimum demand satisfaction, and incorporating facility capacity based on specific scenarios to enhance resiliency. The study involves various stakeholders, including suppliers and manufacturers. The main purpose of utilizing BCT is to facilitate swift transactions between components and support risk-averse decision-making. Integrating BCT as an agility instrument resulted in a 16.93% cost reduction for VSSOARR compared to scenarios where BCT was not considered. The research suggests incorporating viability with BCT positively impacts the supply chain’s overall performance. A sensitivity analysis was conducted on the conservatism coefficient, confidence level, agility rate, demand variation, and problem size.

‘Do no harm’? Rethinking risk and harm narratives in abuse-focused research with children

Despite increasing recognition of children's right to have a say about matters that affect them (Article 12, United Nations Convention on the Rights of the Child), this right is often denied in the context of child abuse research. This article explores the ways in which ethical decision-making can contribute to the denial of this right and the negative implications of this at both an individual and societal level.Ethical decision-making that stymies the conduct of abuse-focused research with children is usually justified with reference to protecting participants from risk of harm. Whilst in no way suggesting that this is not a critical consideration, the authors question the simplistic and deterministic ways in which this can be understood within ethical decision-making, and the unnecessarily risk-averse decisions that can ensue.Sharing examples from their cumulative 30 years' experience of engaging children and young people in abuse-focused research, the authors stress the need for a more holistic, nuanced and dynamic approach to assessing and managing risk of harm. This would consider risks of both inclusion and exclusion. Understanding that risk and harm are neither static nor universally experienced concepts, it would recognise the implausibility of the ‘do no harm’ guarantees often expected of social researchers. Instead, informed by rights-respecting and trauma-informed perspectives, it would focus on holistically promoting participant wellbeing in, and through, research. Key to this is permitting, and supporting, researchers to exercise contextually-informed, collaborative decision-making in the field; something the authors share their emerging practice framework for.

Risk-averse Reinforcement Learning for Portfolio Optimization

This investigation explores Reinforcement Learning (RL) for dynamic portfolio optimization with risk assessment. The challenges include market complexity, uncertain reactions, and regulatory requirements for risk-averse decisions. Our solution leverages Bayesian Neural Network (BNN) to capture uncertainties. We successfully implemented a risk-averse Reinforcement Learning algorithm, achieving 18 percent lower risk. Reinforcement Learning with risk-aversion shows promise for optimizing portfolios for risk-averse investors.

Optimal resilience-based restoration of a system subject to recurrent dependent hazards

Many engineered systems are faced with multiple natural and manmade hazards during operation. Since such hazards may cause system disruptions (e.g., catastrophic failure and performance deterioration), timely and effective restoration needs to be conducted to minimize the negative impacts of the hazards. In practice, these hazards are recurrent and often interact dynamically with each other. This causes a major challenge in assessing the performance of the systems and determining the optimal restoration policy from a holistic perspective. In this paper, we model the availability and resilience of a system subject to recurrent and dependent hazards and develop the optimal restoration policies for decision makers with different risk preferences. Particularly, we first model the random occurrence times and severities of a set of hazards whose occurrence frequencies and severities are dynamically dependent. Next, we derive the analytical expressions for the system's availability metrics. Third, we investigate the optimal restoration policies for both risk-neutral (RN) and risk-averse (RA) decision makers by solving two related optimization problems. A case study on the pipelines in the Mid-Atlantic area of the U.S. is provided to illustrate the practical values of the proposed availability assessment and resilience optimization methods.

Optimal scheduling of a microgrid based on renewable resources and demand response program using stochastic and IGDT-based approach

Optimal economic scheduling of microgrids with photovoltaic (PV) and wind generation has gained increased attention during recent years. Integration of renewable energy resources in microgrids requires increasingly active control and management of energy storages and demand response (DR). In this paper, a risk-based stochastic optimal energy management model is developed for microgrid with renewables, energy storage and load control by time-of-use-based DR programs. Microgrid includes PV system, wind system, micro-turbine, fuel cell, electric vehicle (EV), and energy storage. Information-gap decision theory (IGDT) is employed to address the uncertainty of loads and to provide the operating strategies for the microgrid controllable energy resources. This proposed model has been solved as a mixed-integer non-linear programming (MINLP) in General Algebraic Modeling System (GAMS) software and simulation results in different conditions are studied and discussed. Three different risk management strategies have been studied such as risk-averse, risk-neutral and risk-seeker mode. The simulation results indicate that the impacts of risk-averseness or risk-seeker of the decision maker affect the system operation. For instance, the results showed the DR program's role in risk-averse and risk-taking strategies, impacting consumption and costs. The proposed model ensures the risk-averse decision-maker that if the uncertain parameter deviates within the optimum robustness region, the final cost will not exceed the critical cost. On the other hand, the risk-seeking decision-maker can reach lower final costs by accepting the risks if the uncertain parameter deviates favorably within the opportunity region. Decision-makers can manage risks by adjusting consumption. Thus, considering the cost of risk management is crucial, as it increases with robust or opportunistic approaches.

Procurement portfolio management for resilient supply chains: value of information for risk averse decision making

AbstractIn this study, we investigate the replenishment decisions for firms that rely on multiple sources of supplies, including regular forward contracts, option contracts, and the spot market. Our research aims to tackle the challenges arising due to supplier disruptions and the volatility of spot market prices that are correlated with these disruptions. We develop and solve multi-stage stochastic programming models that incorporate demand and supplier disruption information updates, while considering both risk-neutral and risk averse (CVaR) objectives. These models assist organizations with varying risk attitudes in achieving maximum performance by optimally selecting a procurement portfolio based on the availability and quality of updated information. Through analytical solutions and extensive numerical studies, our findings offer novel insights to organizations and policymakers, empowering them to enhance their supply chain resilience during critical supply disruption situations. Therefore, this research has broader implications and aligns with the multiple sustainability objectives outlined in the Sustainable Development Goals (SDGs).

Should Longtermists Recommend Hastening Extinction Rather Than Delaying It?

Abstract Longtermists argue we should devote much of our resources to raising the probability of a long happy future for sentient beings. But most interventions that raise that probability also raise the probability of a long miserable future, even if they raise the latter by a smaller amount. If we choose by maximising expected utility, this isn’t a problem; but, if we use a risk-averse decision rule, it is. I show that, with the same probabilities and utilities, a risk-averse decision theory tells us to hasten human extinction, not delay it. What’s more, I argue that morality requires us to use a risk-averse decision theory. I present this not as an argument for hastening extinction, but as a challenge to longtermism.

Executive characteristics as moderators: Exploring the impact of geopolitical risk on capital structure decisions

This study examines the relationship between geopolitical risk and firms’ capital structure decisions, focusing on the moderating effects of executive characteristics. Using US corporations data from 1992 to 2020, we find that firms adopt more conservative capital structure choices in response to higher exposure to geopolitical risk. Furthermore, we investigate how executives’ age and gender influence this relationship. Our findings indicate that firms with a higher proportion of female executives demonstrate heightened sensitivity to geopolitical risk, leading to more risk-averse financial decisions. In contrast, firms with older executives attenuate the adverse impact of geopolitical risk on financial leverage, suggesting a mitigating effect. The robustness of our results is confirmed through alternative measures of capital structure and estimation methods. We also identify risk aversion as a potential channel through which geopolitical risk affects capital structure choices and examine the role of executive characteristics in this channel using a moderated mediation model.

Business model innovation decisions: the role of group biases and risk willingness

PurposeManagers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.Design/methodology/approachEmpirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.FindingsFirst, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.Practical implicationsThis paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.Originality/valueThis qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.

Research on Stock Market Decision Making based on Price-to-Earnings Ratio–Taking Shenzhen stock as an example

Taking Shenzhen stock as an example, this paper explores the opportunity for high returns in the stock market through the study of the price-to-earnings ratio, and finally determines the optimal stock market decision. By trying different numerical value on the price-to-earnings ratio and calculating the total return based on historical data, it is obtained that buying when the price-to-earnings ratio is lower than 156 and selling when it is higher than 224.5 can get the highest return. However, in the following general study of high-yield decisions, it is found that all decisions are concentrated in the rapid rise in stock prices caused by “reform cattle” in 2015 in China. Therefore, in order to study more general strategies, the scope and difference of price-to-earnings ratio are redrawn and two feasible decision-making methods are obtained: risk-averse decision-making and risk-preference decision-making. Both two decisions are suitable in a relatively stable stock market, and can earn almost the same profit theoretically, so investors can choose any of them based on their preferences.

Reinforcement Learning With Data Envelopment Analysis and Conditional Value-At-Risk for the Capacity Expansion Problem

The capacity expansion problem is solved by accurately measuring the existing demand-supply mismatch and controlling the emissions output, considering multiple objectives, specific constraints, resource diversity, and resource allocation. This article proposes a reinforcement learning (RL) framework embedded with data envelopment analysis (DEA) to generate the optimal policy and guide the productivity improvement. The proposed framework uses DEA to evaluate efficiency and effectiveness for reward estimation in RL, and also assesses conditional value-at-risk to characterize the risk-averse capacity decision. Instead of focusing on short-term fluctuations in demand, RL optimizes the expected future reward with sequential capacity decisions over time. An empirical study of U.S. power generation validates the proposed framework and provides the managerial implications to policy makers. The results show that the RL agent can successfully learn the optimal policy through observing the interactions between the agent and the environment, and suggest the capacity adjustment that can improve efficiency by 8.3% and effectiveness by 0.9%. We conclude that RL complements productivity analysis, and emphasizes ex-ante planning over ex-post evaluation.

Identifying optimal decision-making strategies and determining effective messaging to maximize the expected outcomes of potential human–extraterrestrial encounters

Abstract The question of whether extraterrestrials exist has driven both the search for extraterrestrial intelligence (SETI) and some attempts of messaging to extraterrestrial intelligence (METI). Nevertheless, no data-driven or theory-based behavioural policy has been suggested. Here we simulate a comprehensive set of human–extraterrestrial strategic interactions, modelled as two-by-two game-theoretic matrices. We examine a sample of possible outcomes by relying on the theory of subjective expected relative similarity (SERS), which takes into account both the expected payoffs and the extent of strategic similarity – the prospects of the opponent making identical choices. Simulation results suggest: focusing messaging efforts on signalling of complete strategic similarity, monitoring potential alien communications for similarity-indicating signals, and using risk-averse decision rules for policy planning and decision-making. The discussion puts forward three guidelines for METI initiatives and addresses the relevance of the findings to human conflict management.