Decision Theory Research Articles

Optimal scheduling of a community Multi-Energy system in energy and flexible ramp markets considering Vector-Coupling storage Devices: A hybrid Fuzzy-IGDT/Stochastic/Robust optimization framework

Community multi-energy systems (CMESs) are emerging as a pivotal solution to address the pressing issues of the current energy crisis and environmental pollution. This paper introduces a comprehensive multi-horizon risk-averse scheduling strategy for CMESs, targeting participation in energy and flexible ramp markets while meeting the demands of electric, natural gas, and hydrogen vehicle charging stations, data centers, and residential and commercial buildings. The proposed CMES incorporates power-to-x vector coupling storage (PtX-VCS) systems, enabling the storage of electricity in various forms to cater to multi-energy demands. Additionally, an integrated demand response (IDR) program, incorporating incentive and load-shifting strategies, is applied to enhance operational efficiency and system flexibility. To manage uncertainties effectively, a hybrid stochastic/robust/fuzzy information gap decision theory (IGDT) method is employed. This approach, formulated as a tri-level optimization model, is transformed into a single-level model using strong duality theory. The results demonstrate a notable reduction in daily operational costs by approximately 5% and 4% through the implementation of the IDR program and PtX-VCS technologies, respectively. Moreover, the CMES’s potential to participate in the flexible ramping market leads to an approximately 7% reduction in total operational costs.

Framework for rational decision-making in bridge maintenance

Bridges are deteriorating, and their safety must be continuously assessed. Efficient management of bridges is essentially about making decisions on maintenance activities in line with the decision maker’s preferences. In other words, structural safety should be ensured while optimally allocating resources. In theory, methods are available for solving this complex task; however, they have not yet been implemented by bridge owners. In this study, the implementation into practice is facilitated by presenting a consistent framework for rational decision-making. It is argued that structural assessment is a decision problem that infrastructure owners are obliged to take responsibility for. Bayesian decision theory is presented as the theoretically ideal decision-making framework. The main components of the framework—representations of structural reliability and failure consequences—are discussed in detail, and human safety constraints are integrated. For demonstration purposes, the decision-making framework is applied to two case studies. In addition, it is parameterised by the relative cost of the intervention to failure and the current failure probability to be applicable for any assessment situation. In that way, this study contributes to more efficient maintenance of single bridges and the development of generally applicable structural codes for assessing existing structures.

Linear Consensus Protocol Based on Vague Sets and Multi-Attribute Decision-Making Methods

This paper proposes a linear consensus protocol QuickBFT based on Vague sets and multi-attribute decision-making methods. QuickBFT simplifies the communication process based on the HotStuff protocol, reduces the four-stage communication to three-stage communication, and reduces the consensus delay. Furthermore, we introduce the Vague set and multi-attribute decision-making theory into the consensus protocol and propose a new leader node selection algorithm, which can prevent Byzantine nodes from becoming leader nodes, thereby improving the protocol performance when the leader node is attacked. Experimental results show that the throughput of QuickBFT is slightly higher than that of the HotStuff protocol without Byzantine nodes, and the consensus delay is reduced by 20%. In the presence of Byzantine nodes, the throughput of QuickBFT is increased by 80% compared with the HotStuff protocol, and the consensus delay is reduced by 60%.

Egyptology and fanaticism

Various decision theories share a troubling implication. They imply that, for any finite amount of value, it would be better to wager it all for a vanishingly small probability of some greater value. Counterintuitive as it might be, this fanaticism has seemingly compelling independent arguments in its favour. In this paper, I consider perhaps the most prima facie compelling such argument: an Egyptology argument (an analogue of the Egyptology argument from population ethics). I show that, despite recent objections from Russell (Noûs, 2023) and Goodsell (Analysis 81(3):420–426, 2021), the argument’s premises can be justified and defended, and the argument itself remains compelling.

Operation optimization considering multiple uncertainties for the multi-energy system of data center parks based on information gap decision theory

Operation optimization considering multiple uncertainties for the multi-energy system of data center parks based on information gap decision theory

Analysis of accidents in maritime transport using the method of Bayesian trust networks

Maritime incidents, though rare, have a significant impact on both the global economy and the environment. Improving maritime navigation practices always requires new ways to enhance safety, inevitably involving learning from past experiences and mistakes. In this context, probabilistic analysis of incidents and their associated consequences can play a crucial role in creating a safer and more efficient maritime transportation system. Bayesian networks constitute a class of probabilistic models based on statistics, decision theory, and graph theory. This paper describes the analysis of maritime incident statistics by selecting probabilistic parameters influencing the risk of their occurrence. Important parameters from this database are grouped, and a Bayesian network is constructed to illustrate the relationships between them. This, in turn, provides insight into the dependencies existing among the variables in the database and the fundamental reasons for these accidents. The data for this study are based on the Lloyds Register and IMO incident databases from 1990 to 2022. Key factors from this database are grouped, and a Bayesian network is built to show the relationships between the corresponding variables, providing an understanding of the probabilistic dependencies among the variables in the database and the primary causes of these incidents.

The shutdown problem: an AI engineering puzzle for decision theorists

AbstractI explain and motivate the shutdown problem: the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don’t try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. I prove three theorems that make the difficulty precise. These theorems suggest that agents satisfying some innocuous-seeming conditions will often try to prevent or cause the pressing of the shutdown button, even in cases where it’s costly to do so. I end by noting that these theorems can guide our search for solutions to the problem.

A novel Hellinger distance-based regret theory method for spherical fuzzy decision making model and its application in logistics

Actual decision making problems are often based on the company decision maker’s behavior factors, such as risk attitude, subjective preference, etc. Regret theory can well express the behavior of the decision maker. In this pursuit, a novel decision making method was developed, based on the regret theory for the multi-attribute decision making problem, in which attribute values were expressed by spherical fuzzy numbers. Distance measurement not only has extensive applications in fields such as pattern recognition and image processing, but also plays an important role in the research of fuzzy decision theory. The existing distance measures of spherical fuzzy set either have special cases of anti-intuition or are more complex in calculation, so finding suitable distance measures is also an important research topic in the decision-making theory of spherical fuzzy set. For this reason, we first establish a new distance of spherical fuzzy sets based on Hellinger distance of probability distribution. A decision maker’s perception utility value function is proposed using the new distance formula, which is used to measure the regretful and rejoice value. Then we establish an optimization model for solving the attribute weights, when the information of attribute weight was partially known. Subsequently, the comprehensive perceived utility values were utilized to rank the order of the alternatives. Finally, a numerical example of assessment of logistics providers is used to show that the new decision making method is effective and feasible.

Fast & slow decisions under risk: Intuition rather than deliberation drives advantageous choices

Would you take a gamble with a 10% chance to gain $100 and a 90% chance to lose $10? Even though this gamble has a positive expected value, most people would avoid taking it given the high chance of losing money. Popular “fast-and-slow” dual process theories of risky decision making assume that to take expected value into account and avoid a loss aversion bias, people need to deliberate. In this paper we directly test whether reasoners can also consider expected value benefit intuitively, in the absence of deliberation. To do so, we presented participants with bets and lotteries in which they could choose between a risky expected-value-based choice and a safe loss averse option. We used a two-response paradigm where participants made two choices in every trial: an initial intuitive choice under time-pressure and cognitive load and a final choice without constraints where they could freely deliberate. Results showed that in most trials participants were loss averse, both in the intuitive and deliberate stages. However, when people opted for the expected-value-based choice after deliberating, they had predominantly already arrived at this choice intuitively. Additionally, loss averse participants often showed an intuitive sensitivity to expected value (as reflected in decreased confidence). Overall, these results suggest that deliberation is not the primary route for expected-value-based responding in risky decision making. Risky decisions may be better conceptualized as an interplay between different types of “fast” intuitions rather than between two different types of “fast” and “slow” thinking per se.

Considering personalized individual semantics with ordinal and cardinal consensus reaching processes via three-way decision and regret theory

The seamless integration of computerized methodologies into industrial engineering problem-solving is pivotal for optimizing efficiency. In the specific domain of multi-attribute group decision-making (MAGDM) with probabilistic linguistic term sets (PLTSs), these methodologies offer systematic approaches to consensus building, ensuring effective decision processes in intricate scenarios. Within the realm of PLTSs, the consensus-reaching process (CRP) for MAGDM is gaining prominence. This paper addresses this evolving area by proposing ordinal and cardinal CRPs within the framework of PLTSs, specifically incorporating the regret theory (RT) of three-way decisions (TWD). The paper introduces an initial distance formula under PLTSs, providing a complementary approach to assess similarity relations among decision-makers (DMs). To account for diverse semantics across DMs, personalized individual semantics (PIS) is integrated into the CRP, recognizing variations in DMs’ alternatives and attributes. To enhance realism, the paper introduces the concepts of individual alternative sets and individual attribute sets. Additionally, the paper integrates ordinal and cardinal consensus, establishing a dynamic feedback adjustment mechanism grounded in the principles of RT and TWD. The method’s reasonableness is validated through a real case study, and a comparative analysis with the existing methods underscores the superiority of the approach presented in this paper.

Optimal scheduling of regional integrated energy systems with hot dry rock enhanced geothermal system based on information gap decision theory

AbstractHot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions.

Hybrid seismic vulnerability models for regional structures considering bivariate intensity measures

The multiple-probability decision-making method for earthquake hazards considers multiple performance indicators and can be used to effectively estimate the seismic risk of structures. The seismic vulnerability model directly affects the development of the earthquake risk distribution in regional structures. This paper innovatively developed a bivariate intensity measurement method based on macroscopic and instrumental seismic intensities. An optimized instrument intensity calculation model was established considering the actual monitoring data of historical earthquakes in China. A hybrid seismic vulnerability quantification method based on empirical, expert judgement and analysis has been proposed and applied to evaluate the vulnerability levels of three types of building clusters in two typical earthquakes in Qinghai Province, China (the 2008 Dachaidan (DCD) earthquake and the 2009 Haixi (HX) earthquake). A structural hybrid vulnerability matrix and surface for nine zones based on bivariate intensity measures were developed. A synthetic vulnerability membership index considering fuzzy failure criteria and decision theory was proposed, and vulnerability membership curves for different vulnerability levels were established. An improved earthquake economic loss prediction function is proposed, and synthetic economic vulnerability index prediction models for three typical buildings in different regions are established. The estimated and calculated results of the developed multiscale variable parameter synthetic vulnerability model are highly consistent with the actual earthquake loss observations.

Successful Surfing in Capital Market: Phenomenological Study of Indonesian “Warren Buffett”

Objective: The purpose of this study is to uncover the successful secrets of a No. 1 individual investor on the Indonesian stock exchange who has proven to earn huge amounts of profits. Theoretical Framework: Some theories related to investor behavior theory, among others, are: Regret Theory, this theory is a development of previous decision-making theories, namely prospect theory and expected usability theory. Method: This research explores the philosophy, behavior, strategies, techniques, and ways to find stocks that can provide great returns based on the experience of an individual investor. This research also examines in depth the experience of a famous investor named Lo Kheng Hong (LKH), known as Warren Buffett of Indonesia. Results and Discussion: The results showed that the factors that support the success of LKH in investing in the IDX can be: (1) reading the company's financial statements; (2) analyzing financial data; considering the integrity of its owners and management; (3) investing by buying shares on the IDX; (4) daring to make decisions to buy shares based on their own analysis; (5) patiently waiting; (6) simple to live their lives and frugal. Research Implications: One of the secrets of success, the strategy in finding stocks that provide big profits is to read and look at financial statements, analyze deeply using simple fundamental analysis, look for stocks that are undervalued. Originality/Value: This study contributes to literatures regarding how to be successful investor in stock market.

Neural underpinnings of ethical decisions in life and death dilemmas in naïve and expert firefighters

When a single choice impacts on life outcomes, faculties to make ethical judgments come into play. Here we studied decisions in a real-life setting involving life-and-death outcomes that affect others and the decision-maker as well. We chose a genuine situation where prior training and expertise play a role: firefighting in life-threatening situations. By studying the neural correlates of dilemmas involving life-saving decisions, using realistic firefighting situations, allowed us to go beyond previously used hypothetical dilemmas, while addressing the role of expertise and the use of coping strategies (n = 47). We asked the question whether the neural underpinnings of deontologically based decisions are affected by expertise. These realistic life-saving dilemmas activate the same core reward and affective processing network, in particular the ventromedial prefrontal cortex, nucleus accumbens and amygdala, irrespective of prior expertise, thereby supporting general domain theories of ethical decision-making. We found that brain activity in the hippocampus and insula parametrically increased as the risk increased. Connectivity analysis showed a larger directed influence of the insula on circuits related to action selection in non-experts, which were slower than experts in non rescuing decisions. Relative neural activity related to the decision to rescue or not, in the caudate nucleus, insula and anterior cingulate cortex was negatively associated with coping strategies, in experts (firefighters) suggesting practice-based learning. This shows an association between activity and expert-related usage of coping strategies. Expertise enables salience network activation as a function of behavioural coping dimensions, with a distinct connectivity profile when facing life-rescuing dilemmas.

Regret-based three-way decisions with set pair analysis in incomplete information systems

Set pair analysis is a valuable tool for managing uncertain systems and can effectively handle missing information in the decision-making process. In addition, regret theory can objectively describe the impact of decision-maker psychology on decision-making. However, traditional decision-making methods rarely combine set pair analysis and regret theory to discuss decision problems. Thus, this article aims to integrate regret theory into the three-way decision theory based on set pair analysis, presenting a novel approach to address multi-attribute decision-making problems in real life. Specifically, we first propose a data-driven approach based on set pair analysis to determine attribute weights. Then, a similarity relationship is established based on the weighted distance to calculate conditional probability. Meanwhile, the average value of regret and joy under each attribute is selected as a reference point to determine the relative utility function using regret theory. Subsequently, we develop a new three-way decision method in incomplete information systems and apply it to solve practical problems. Finally, the effectiveness and superiority of our method are demonstrated by case analysis and comparison. We also conduct sensitivity analysis to examine the stability of the new method.

Influence of Artificial Intelligence on Credit Risk Assessment in Banking Sector

Purpose: The aim of the study was to examine the influence of artificial intelligence on credit risk assessment in banking sector. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that AI-driven models demonstrate superior performance in identifying risky borrowers and capturing complex credit risk patterns compared to traditional methods. Additionally, the integration of explainable AI (XAI) techniques has enhanced transparency and interpretability in credit risk assessment processes, facilitating better understanding among stakeholders and improving decision-making transparency. Unique Contribution to Theory, Practice and Policy: Decision theory & technology acceptance model (TAM) may be used to anchor future studies on influence of artificial intelligence on credit risk assessment in banking sector. Continuously invest in research and development to advance the theoretical understanding of AI-driven credit risk assessment models. This includes exploring the integration of machine learning with behavioral economics theories to better predict borrower behavior and default probabilities. Encourage banks to adopt a hybrid approach that combines the strengths of AI-driven models with human expertise. Develop comprehensive regulatory guidelines and standards to govern the use of AI in credit risk assessment and ensure ethical and responsible practices. This includes establishing transparent model validation and governance frameworks to mitigate the risks of algorithmic bias, data privacy violations, and discriminatory lending practices. Regulatory authorities should also promote industry-wide collaboration and knowledge sharing to foster innovation while safeguarding consumer interests and financial stability.

Decision Theory, Subjective Uncertainty, and Computer Science

I argue that further integration between Decision Theory and the methods of quantifying complexity and evaluating performance in Computer Science is valuable. I review [Lanzani 2024] as an illustration of this combination.

Impact of Ethical Dilemmas on Decision Making in Ambiguous Situations among Police Employees

This research explored the relationship between ethical dilemmas and decision-making in ambiguous situations among n=200 police officers in Punjab, Pakistan, employing a mixed-methods approach that integrated quantitative surveys and qualitative interviews. Guided by ethical decision-making and psychological theories, the study examined how organizational support and years of experience influence officers' ability to navigate ethical challenges. The findings reveal that while higher organizational support was slightly linked to improved ethical decision-making, this relationship was not statistically significant. Conversely, more excellent experience was associated with enhanced decision-making capabilities, though no strong correlation was found between the frequency of ethical dilemmas and decision-making effectiveness. Psychological factors, including stress, cognitive biases, and emotional responses, significantly impact decision-making processes, supporting the hypothesis that these elements are critical in shaping how officers handle ambiguous situations. This study is crucial as it provides a nuanced understanding of how organizational and psychological factors contribute to ethical decision-making in law enforcement. It highlights the need for enhanced organizational support systems, continuous professional development, and targeted psychological interventions, such as stress management and cognitive bias training, to equip officers for ethical decision-making better. By addressing the gaps identified and proposing directions for future research, this study offers valuable insights into improving ethical practices within police agencies, ultimately contributing to more effective and moral law enforcement.

Affordances in crop diversification: Three cases from the Philippines

This paper investigated the viability of crop diversification as an alternative production system for Filipino rice farmers in light of the Rice Tariffication Law. Qualitative research methods were employed, including focus group discussions, key informant interviews, in-depth interviews, and wind-screen survey. The transcript analysis employed both inductive and deductive approaches, supplemented by auditability measures such as coding guides and informal member-checking. Using the Affordances Theory with additional insights from the Capabilities Theory and Intuitive Decision-Making Theory, the findings show that rice farmers in Nueva Ecija, Ilocos Norte, and Tarlac shared one cognitive process and one set of variables in deciding on diversifying crops. This is despite the provincial differences in farming contexts. Affordances and anti-affordances in crop diversification are dependent on farmers’ visual and tracking experiences, which guide them in their evaluation of crop diversification as a pro-welfare farming practice. They evaluate the suitability of crop diversification for their sociocultural, economic, and farming contexts, but then shift to intuitive decision-making for their ultimate decision. Risks that are mostly external to farmer control, such as sufficiency of irrigation, financial capital, weather, and climate, make them conclude in a posture of luck after a detailed evaluation process on the practice of crop diversification. This study provides insights into using crop diversification as an alternative production system for rice farmers.

Evidential Decision Theory and the Ostrich

Evidential Decision Theory is flawed, but its flaws are not fully understood. David Lewis (1981) famously charged that EDT recommends an irrational policy of managing the news and “commends the ostrich as rational”. Lewis was right, but the case he appealed to—Newcomb’s Problem—does not demonstrate his conclusion. Indeed, decision theories other than EDT, such as Committal Decision Theory and Functional Decision Theory, agree with EDT's verdicts in Newcomb’s Problem, but their flaws, whatever they may be, do not stem from any ostrich-like recommendations. We offer a new case which shows that EDT mismanages the news, thus vindicating Lewis’s original charge. We argue that this case reveals a flaw in the “Why ain’cha rich?” defense of EDT. We argue further that this case is an advance on extant putative counterexamples to EDT.