Complex Decision Research Articles

Colourful network: pair-bonding temporal dynamics involve sexual signals and impact reproduction

Abstract Mate choice and pair-bonding are the products of complex decisions involving repeated social interactions over time. They generally rely on multiple sexual signals and behaviours, depend on the phenotype and experiences of the chooser and are affected by environmental conditions and competition for mate access. However, studies investigating the mechanisms of mate selection often ignore the dynamic aspects of the pair formation. In the current study, we used social network analyses to (1) describe sexual interactions over time in multiple groups of captive zebra finches (Taeniopygia guttata), (2) ask how individual sexual signals and body condition relate to pair-bonding temporal dynamics and (3) investigate whether sexual networks influence assortative mating and reproductive performance. We followed sexual interactions of 8 sex-balanced groups of 8 individuals to extract social network metrics over 8 sessions of observation. We assessed individual body condition, sexual signals and couples’ pairing latency, laying date, clutch size and embryo viability. Pair-bonding dynamics were first characterized by song interactions between most individuals, then by numerous allopreening and clumping behaviours, targeting more specific partners. More colourful individuals became engaged in more sexual interactions more rapidly, and such network dynamics explained assortative mating for beak colour. Interestingly, being involved in song interactions was negatively associated with reproductive performances while the reverse was true for being involved in allopreening and clumping interactions. Our study highlights the need to study pair-bonding dynamics to better understand how variations in individual phenotype within sexual networks explain assortative mating and reproductive performance in monogamous species.

A cooperative jamming decision-making method based on multi-agent reinforcement learning

Electromagnetic jamming is a critical countermeasure in defense interception scenarios. This paper addresses the complex electromagnetic game involving multiple active jammers and radar systems by proposing a multi-agent reinforcement learning-based cooperative jamming decision-making method (MA-CJD). The proposed approach achieves high-quality and efficient target allocation, jamming mode selection, and power control. Mathematical models for radar systems and active jamming are developed to represent a multi-jammer and multi-radar electromagnetic confrontation scenario. The cooperative jamming decision-making process is then modeled as a Markov game, where the QMix multi-agent reinforcement learning algorithm is innovatively applied to handle inter-jammer cooperation. To tackle the challenges of a parameterized action space, the MP-DQN network structure is adopted, forming the basis of the MA-CJD algorithm. Simulation experiments validate the effectiveness of the proposed MA-CJD algorithm. Results show that MA-CJD significantly reduces the time defense units are detected while minimizing jamming resource consumption. Compared with existing algorithms, MA-CJD achieves better solutions, demonstrating its superiority in cooperative jamming scenarios.

State-of-the-Art Review: Complexities in Cardiac Implantable Electronic Device Infections: A Contemporary Practical Approach.

Cardiac implantable electronic device infections (CIEDIs) present substantial challenges for infectious diseases specialists, encompassing diagnosis, management, and complex decision making involving patients, families, and multidisciplinary teams. This review, guided by a common clinical case presentation encountered in daily practice, navigates through the diagnostic process, management strategies in unique scenarios, long-term follow-up, and critical discussions required for CIEDIs.

Risk aversion can promote cooperation

Abstract Cooperative dynamics are central to our understanding of many phenomena in living and complex systems. However, we lack a universal mechanism to explain the emergence of cooperation. We present a novel framework for modelling social dilemma games with an arbitrary number of players by combining reaction networks, methods from quantum mechanics applied to stochastic complex systems, game theory and stochastic simulations of molecular reactions. Using this framework, we propose a novel and robust mechanism for cooperation based on risk aversion that leads to cooperative behaviour in population games. Rather than individuals seeking to maximise payouts in the long run, individuals seek to obtain a minimum set of resources with a given level of confidence and in a limited time span. We show that this mechanism can lead to the emergence of new equilibria in a range of social dilemma games.

Navigating market entry decisions in the solar PV industry: The role of founders' pre‐entry experience across the value chain

AbstractResearch SummaryIn emerging industries characterized by several technological generations giving rise to multiple submarkets that emerge at different points in time, start‐ups entering with novel technologies encounter markets at varying development stages and are confronted with the choice to enter either a nascent or an established market. We study the solar photovoltaic industry between 1985 and 2017 to examine how founders' pre‐entry experience along the industry value chain helps them navigate this complex decision. We find that founders with upstream industry experience possess operational acumen and manufacturing knowledge, prompting them to favor established markets. Conversely, founders with focal industry experience, guided by their unique insights into new applications and the limitations of existing technologies in fulfilling emerging customer needs, are more inclined to enter nascent markets.Managerial SummaryNew technologies often enable fulfillment of new user needs. In emerging industries witnessing successive waves of technologies, new markets materialize at different point in times. When firms enter these industries with fungible technologies, they can target markets in the early phases of formation or markets that are more mature and well developed. Evidence from the solar PV industry shows that the experience that entrepreneurs gained before founding their start‐up in this industry influences the choice of market: founders with prior experience developing solar technologies enter markets addressing new and undefined user needs while founders with prior experience in upstream industries that supply components to the solar industry gravitate toward mature markets, which place greater value on value efficiency enhancements.

Artificial Intelligence (AI) in Endodontics: A Review

ABSTRACT Artificial intelligence (AI) holds the promise of mimicking human intelligence to enhance prediction and complex decision-making in healthcare, carving out a significant role in dentistry tasks, notably endodontics. It has shown remarkable accuracy in detecting and predicting diseases within this field, potentially refining diagnostics and treatments to boost endodontic success rates. However, there’s a need to validate the reliability, practicality, and cost-effectiveness of AI before fully integrating it into everyday clinical settings. This review aims to explore AI’s current applications in endodontics and possible future developments.

AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents

In recent years, the interplay between AI agents and blockchain has enabled secure and scalable collaboration among multi-agent systems, promoting unprecedented levels of autonomy and interoperability. AI agents play a vital role in facilitating complex decision making and improving operational efficiency in blockchain systems. This collaborative synergy is particularly evident in how multi-agent systems collectively tackle complex tasks to ensure seamless integration within these frameworks. While significant efforts have been made to integrate AI agents and blockchain, most studies overlook the broader potential of AI agents in addressing challenges such as interoperability, scalability, and privacy issues. In this paper, we bridge these gaps by illustrating the interplay between AI agents and blockchain. Specifically, we explore how AI agents enhance decentralized systems and examine blockchain’s role in enabling secure and scalable collaboration. Furthermore, we categorize practical applications across domains, such as Web3, decentralized finance (DeFi), asset management, and autonomous systems, providing practical insights and real-world use cases. Additionally, we identify key research challenges, including the complexities of multi-agent coordination, interoperability across diverse systems, and privacy maintenance in decentralized frameworks. Finally, we offer future directions in terms of governance, sovereignty, computation, and interpretability to promote a secure and responsible ecosystem.

Low dimensionality of phenotypic space as an emergent property of coordinated teams in biological regulatory networks.

Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks and corresponding transcriptomic data are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes. We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. Using artificial networks of varied topologies, we also establish the conditions for generating canalized cell-fate landscapes, offering insights into diverse binary cellular decision-making networks.

Complex decision making for individual patients with penile cancer: benchmarking divergent practices in European high-volume reference centers - results from eUROGEN survey

Background and objectivesPenile cancer (PeCa) remains a challenge due to its rarity and the lack of prospective studies, leading to treatment challenges and controversies. Guidelines offer recommendations, but discrepancies with clinical practice persist. This study analyzed treatment practices among specialists managing high-risk PeCa in European reference centers. MethodsA cross-sectional survey included 39 PeCa specialists from 13 European countries representing high-volume centers. Descriptive analysis assessed (neo)adjuvant therapy preferences, systemic regimen choices, immunotherapy use, and next-generation sequencing (NGS) integration. Key findings and limitationsVariations in managing high-risk PeCa, especially in (neo)adjuvant therapy utilization, were noted among participants. The differences highlight the influence of professional backgrounds and variations in treatment approaches between participants. Systemic regimen preferences and immunotherapy utilization also varied. Limited NGS integration indicated gaps in precision medicine adoption. Limitations included sample size, self-reported data, and cross-sectional design. Conclusions and clinical implicationsThis study offered insights into PeCa management by specialists in high-volume European reference centers, stressing the need for evidence-based recommendations, guideline adherence, and collaboration to enhance PeCa care. Patient summaryManaging PeCa is complex due to its rarity and treatment controversies. This study examined practices among specialists in European reference centers, revealing treatment variations. The findings emphasize the importance of evidence-based care and collaboration in optimizing PeCa management. Micro-AbstractOur aim was to assess treatment practices for high-risk penile cancer (PeCa) among specialists in high volume European reference centres, addressing the challenges arising from the rarity of this disease and the limited prospective data. Our survey of 39 PeCa specialists from 13 European countries revealed notable differences in (neo)adjuvant therapy, systemic treatment regimens and the use of immunotherapy. In particular, the limited integration of next-generation sequencing indicates gaps in the adoption of precision medicine. These findings highlight the discrepancies between clinical practice and guidelines and emphasize the need for evidence-based recommendations and improved collaboration in the treatment of PeCa.

Large language models: Tools for new environmental decision-making.

This study represents the first exploration of Large Language Models (LLMs) in environmental decision-making, examining their potential benefits and limitations. To address environmental issues, we propose and compare two generalizable frameworks: an LLMs-assisted framework that leverages LLMs to augment human expertise and coding in traditional decision workflows, and an LLMs-driven framework that aims to automate optimization. Through a water engineering case study focusing on PFAS control, where Environmental Fluid Dynamics Code (EFDC) was used for water environment simulation, we illustrate how to instantiate these frameworks and assess their performance. The case study reveals generalizable insights about these frameworks. Results indicate that both frameworks can contribute to environmental decision-making optimization to varying degrees, though their applicability differs significantly when facing complex decision scenarios. The LLMs-assisted framework, which effectively regulates flow rates and achieves higher PFAS interception, demonstrates how AI can enhance human decision-making while preserving the essential role of domain expertise and professional judgment. In contrast, the LLMs-driven framework faces challenges in handling complex parameter optimization tasks due to constraints such as context window and maximum output length. The findings emphasize the advantages of integrating Artificial Intelligence (AI) with conventional environmental modeling and management practices. This work confirms a crucial principle: LLMs should enhance rather than replace human expertise, with the ultimate responsibility for environmental decisions remaining with humans. The originality of this research lies in its innovative methodological approach, which leverages process design and prompt engineering to integrate cutting-edge AI with conventional environmental models, establishing a foundation for responsible human-AI collaboration in environmental decision-making. While examining current strengths and limitations, this framework robustly generates optimized environmental decision strategies, marking a new exploration in the field.

The Influence of Words of Mouth, Company Credibility, and Price on the Decision to Purchase Ship Repair Services from Shipyard Companies

Consumer decision-making varies depending on the types of purchasing decisions; more complex decisions may involve more participation and greater freedom to buy. The purpose of this study was to determine the effect of word of mouth on purchase intention, the effect of word of mouth on purchase decision mediated by purchase intention, the effect of word of mouth on purchase decision, the effect of price on purchase intention, the effect of price on purchase decision mediated by purchase intention, the effect of price on purchase decision, the effect of credibility on purchase intention, the effect of credibility on purchase decision mediated by purchase intention, the effect of credibility on purchase decision and the effect of purchase intention on purchase decision. This type of research is quantitative research. The sample in this study were companies or individuals who owned ships, shipping agents, or ship management who had carried out ship repairs at PT PAL Indonesia. As many as 100 respondents used purposive sampling techniques. The analysis technique in this study used Warp Partial Least Square (Warp PLS) analysis. The results of the analysis in this study are that there is a significant influence of word of mouth on purchase intention, there is no significant influence of word of mouth on purchasing decisions mediated by purchase intention, there is a significant influence of word of mouth on purchasing decisions, there is a significant influence of price on purchasing decisions mediated by purchase intention, there is a significant influence of price on purchasing decisions, there is a significant influence of credibility on purchasing intentions, there is no significant influence of credibility on purchasing decisions mediated by purchase intention, there is a significant influence of credibility on purchasing decisions, and there is a significant influence of purchasing intentions on purchasing decisions.

XplainLungSHAP: Enhancing Lung Cancer Surgery Decision Making with Feature Selection and Explainable AI Insights

Background: Lung cancer surgery often involves complex decision-making, where accurate and interpretable predictive models are crucial for assessing postoperative risks and optimizing outcomes. This study presents XplainLungSHAP, a novel framework combining SHAP (SHapley Additive exPlanations) and attention mechanisms to enhance both predictive accuracy and transparency. The aim is to support clinicians in preoperative evaluations by identifying and prioritizing key clinical features. Methods: The framework was developed using data from 470 patients undergoing lung cancer surgery. Key clinical features were identified through SHAP, ensuring alignment with medical expertise. These features were dynamically weighted using an attention mechanism in a neural network, enhancing their impact on survival predictions. The model’s performance was evaluated through accuracy, confusion matrices, and ROC analysis, demonstrating its reliability and interpretability. Results: The XplainLungSHAP model achieved an accuracy of 91.49%, outperforming traditional machine learning models. SHAP analysis identified critical predictors, including pulmonary function, comorbidities, and age, while the attention mechanism prioritized these features dynamically. The combined approach ensured high accuracy and offered actionable insights into survival predictions. Conclusions: XplainLungSHAP addresses the limitations of black-box models by integrating explainability with state-of-the-art predictive techniques. This framework provides a transparent and clinically relevant tool for guiding surgical decisions, supporting personalized care, and advancing AI applications in thoracic oncology.

Experiences and Perceptions of People With Advanced Cancer and Their Family Caregivers of Shared Decision-Making in Palliative Cancer Care.

To explore the experiences and perceptions of people with advanced cancer and their family caregivers of shared decision-making, including how they and nurses are involved. Exploratory interview study. Semistructured, individual interviews conducted in 2023 were analysed using reflexive thematic analysis. Twenty-four participants (15 people with cancer and 9 caregivers) participated in this study. The analysis generated five themes; (1) Decisions are ultimately about living or dying, (2) Desires and expectations for involvement in decision-making, (3) Uncertainty about potential and capacity for shared decision-making, (4) Good quality information and communication are key for involvement and (5) Nurses' contributions are unclear. Overall, our participants were unsure about how decision-making about the complex and existential decisions in palliative cancer care was shared, or how it included them, or nurses. People receiving care could benefit from more clarity on what shared decision-making is, how they can be involved and how nurses can facilitate and support shared decision-making. Part of the nursing role is to support and involve people with cancer and family caregivers in decisions about treatment and care. Our findings suggest that people receiving care may be unclear about the concept of shared decision-making, and so also about how nurses potentially or actually support decision-making. Our study further clarifies the challenges and possibilities for involving all stakeholders in shared decision-making. This additional insight may help in future development and implementation of shared decision-making. COREQ. WHAT DOES THIS PAPER CONTRIBUTE TO THE WIDER GLOBAL CLINICAL COMMUNITY?: Adds to knowledge and understanding of the complexity of shared decision-making in palliative cancer care and how nurses can contribute to this and advancing a person-centred approach. A patient and public involvement group participated in all phases of planning and conducting this study.

A Health-Sector-Specific Occupational Health and Safety Management System Model

Background/Objectives: The health sector is one of the most important sectors, and occupational accidents and occupational diseases that health workers are exposed to are more important compared to those in other sectors. Especially, the increase in health and safety problems faced by hospital workers necessitates the development of an occupational health and safety (OHS) management system model specific to the health sector. Existing health and safety management systems generally do not sufficiently take sectoral dynamics into account, and the adaptation of these standards to the sectors is left to the individual efforts of the users. This situation leads to management systems that lack a sector-specific approach and do not adopt a common language and methodology. Methods: The aim of this study was to create an OHS management system specific to the health sector. While developing the model, AHP and DEMATEL, which are multi-criteria decision-making methods that help people to make complex decisions, were used. Results: By applying these methods, important criteria for the proposed model were determined. The criteria, including “Improvement of the management system of the health institution”, “Determination of control measures”, “Assessment of risks specific to the health institution”, “Identifying hazards specific to the health institution” and “Determining its context”, have been determined as priority criteria and weighted using the AHP and DEMATEL methods. Conclusions: As a result of the findings of this research, designing a unique occupational health and safety (OHS) management system that takes into account the dynamics of the health sector will contribute to the prevention of occupational accidents and occupational diseases in the health sector.

Quantifying the potential value of entomological data collection for programmatic decision-making on malaria control in sub-Saharan African settings

BackgroundThe availability of many tools for malaria control leads to complex decisions regarding the most cost-effective intervention package based on local epidemiology. Mosquito characteristics influence the impact of vector control, but entomological surveillance is often limited due to a lack of resources in national malaria programmes.MethodsThis study quantified the monetary value of information provided by entomological data collection for programmatic decision-making using a mathematical model of Plasmodium falciparum transmission. The 3-year impact and cost of various intervention packages was simulated in different sub-Saharan African settings, including combinations of scaling-up insecticide-treated nets (ITN), switching to next-generation ITNs, and a treatment and prevention package. The DALYs averted and their net monetary benefit were compared at different cost-effectiveness thresholds and the value of resolving uncertainty in entomological model parameters was calculated.ResultsAcross transmission settings and at cost-effectiveness thresholds over US$170 per DALY averted, the most cost-effective intervention package was switching to and scaling up pyrethroid-pyrrole ITNs combined with the treatment and prevention package. The median expected value of perfect information on the entomological indicators was US$0.05 (range 0.02–0.23) and US$0.17 (range 0.09–1.43) per person at risk at thresholds of US$75 and US$1000 per DALY averted, respectively. This represented less than 2% of the net monetary benefit of implementing the most cost-effective intervention package. Value of information estimates at cost-effectiveness thresholds over US$250 were higher than current investments into entomological monitoring by the US President’s Malaria Initiative.ConclusionsThese results suggest that entomological data collection should not delay implementation of interventions with demonstrated efficacy in most settings, but that sustained investments into and use of entomological surveillance are nevertheless worthwhile and have broad value to national malaria programmes.

2839 Decision aids for the Care of Patients with multimorbidity: a systematic review

Abstract Introduction Clinical decision making for older adults with multimorbidity can be complex and demanding. When facing complex decision-making, patients may experience decisional conflicts, leading to low treatment adherence, adverse health outcomes, and increased utilisation of health services, etc. To address these issues, patient decision aids (DAs) have been developed and utilised in the decision-making process to facilitate informed decisions. The aim of this study is to identify DAs developed for patients with multimorbidity and assess their quality. Method We searched full-text papers on nine databases. Any article utilising a DA for patients with multimorbidity was eligible and DAs for making medical decisions at any point were eligible. We used the International Patient Decision Aid Standards (IPDAS) checklist to assess the quality of DAs. Results In total, ten articles including six DAs were included. Two DAs targeted for the older patients with multimorbidity. Most DAs didn’t focus on specific treatment choices but rather aimed at improving the overall quality of life for patients with multimorbidity. The targets of these DAs are including setting goals about health care, preparing for conversation with doctors and taking ownership for the decisions. IPDAS checklist revealed that only one DA met all qualifying criteria and provided comprehensive choice. Three DAs were deemed to have poor quality due to their failure to provide the pros and cons of decisions. The quality of the remaining DAs was difficult to judge due to incomplete versions. Conclusions DAs for patients with multimorbidity were few and had poor quality. Designing DAs for this patient population presents challenges given the complex nature of multimorbidity and its lack of specific treatment options. Future development should focus on adhering to the IPDAS checklist, provide more information and possibility, and aim at improving the quality of life for patients with multimorbidity.

Prevalence of Moral Injury in Nationally Representative Samples of Combat Veterans, Healthcare Workers, and First Responders.

Moral injury affects a variety of populations who make ethically complex decisions involving their own and others' well-being, including combat veterans, healthcare workers, and first responders. Yet little is known about occupational differences in the prevalence of morally injurious exposures and outcomes in nationally representative samples of such populations. To examine prevalence of potentially morally injurious event (PMIE) exposure and clinically meaningful moral injury in three high-risk groups. Cross-sectional survey with responses weighted to national geodemographic benchmarks. Combat veterans, healthcare workers, and first responders (N=1232) in the USA. Moral Injury and Distress Scale (MIDS). Many combat veterans (49.3%), healthcare workers (50.8%), and first responders (41.6%) endorsed exposure to a PMIE. Clinically meaningful moral injury symptoms were endorsed by 6.5% of combat veterans, 7.3% of healthcare workers, and 4.1% of first responders. After adjusting for age, gender, race, and ethnicity, relative to first responders, combat veterans were more likely to endorse transgressing their values by what they did and healthcare workers were more likely to endorse witnessing others' wrongful acts. Additionally, combat veterans (adjusted risk ratio (aRR) = 2.18, 95% confidence interval (95% CI) = 1.09, 2.16) and healthcare workers (aRR = 2.02, 95% CI = 1.03, 3.83) were over twice as likely to screen positive for clinically meaningful moral injury in comparison to first responders. No differences in exposures or outcomes emerged between combat veterans and healthcare workers. Results from these nationally representative samples of three high-risk populations suggest that exposure to PMIEs is common and a sizable minority report clinically meaningful moral injury.

Promoting owners’ shared parking willingness considering management mechanisms in community network

Abstract Shared parking effectively optimizes urban parking resources while making full use of private parking spaces and satisfying the growing demand for parking in large cities. However, some owners are unwilling to share private parking spaces and oppose the community in conducting shared parking projects. To promote the sharing of private parking spaces, we use a complex network evolutionary game method to depict the impact of owners’ unfavorable relationships on individuals’ decision processes in real time and explore the impact of management mechanisms on owners’ willingness to share. The results demonstrate that experience-weighted attraction learning strategies that focus on experiential learning and adaptability are more conducive to promoting owner cooperation, whereas neighbor-avoidance conflict costs resulting from interactions between owners restrict cooperative behavior, and a higher number of network owners is detrimental to cooperation. A platform that improves the rejection rate of parking requests and overtime inconvenience cost is conducive to cooperation, but overtime probability and time window conflict cost reduce owners’ willingness to share. The government can lessen these adverse effects by adding compensation to all owners and increasing the public opinion adjustment coefficient to promote cooperative behavior and increase the number of shared parking spaces.

The importance of pension and financial knowledge for pension plan participation in Italy

Abstract Due to the implementation of several pension reforms, Italian individuals have to take more complex financial decisions and have more responsibilities on their retirement well-being which also includes the choice of whether to participate in pension funds. Relying on novel survey data, we empirically investigate the effect of pension and financial knowledge on the probability of pension plan participation in Italy. Despite documenting the already well-known trends about disparities in the level of such knowledge, we are able to establish that only pension literacy has a positive and causal effect on the probability to participate in a private pension fund.

Power and Energy Transformation: Multi-Criteria Decision-Making Utilizing Complex q-Rung Picture Fuzzy Generalized Power Prioritized Yager Operators

Existing studies in decision-making often face limitations in adequately handling complex and uncertain environments. This study addresses these shortcomings by introducing novel operations and aggregation techniques tailored for complex q-rung picture fuzzy numbers (Cq-RPFNs). Building upon the drawbacks of conventional approaches, we extend Yeager's operations to Cq-RPFNs, enabling comprehensive operations such as addition, multiplication, scalar product, and scalar power. Additionally, two aggregation operators, the Complex q-rung Picture Fuzzy Generalized Power Prioritized Yager Weighted Average, and the Complex q-rung Picture Fuzzy Generalized Power Prioritized Yager Weighted Geometric are developed to overcome the limitations of existing aggregation methods. Rigorous analysis of these operators for properties such as idempotency and monotonicity ensures their robustness in decision-making processes. Through a comprehensive multi-criteria decision-making method utilizing these operators, decision-makers are empowered to navigate complex decision landscapes effectively. Comparative evaluations against existing alternatives highlight the superiority and effectiveness of the proposed approaches. A case study focusing on India's Power and Energy Sector in 2024 demonstrates practical application, supported by numerical examples that validate the methodologies' efficacy. This study addresses the drawbacks of existing approaches and provides valuable insights and tools for decision-makers operating in complex and uncertain domains.