Behavior Of Agents Research Articles

Participation in modeling social-ecological systems across scales in agriculture and forestry

Participatory approaches have gained recognition in scientific research, particularly in modeling human-environmental interactions and social-ecological systems (SES). Despite this achievement, a lack of in-depth understanding prevails to which degree, and at which scales and levels, participation takes place during the modeling cycle. Furthermore, information on the linkages between the level of the participating stakeholders and the modeling level of agents and environmental processes remains scarce. To shed light on this research gap, we conducted a systematic review including 79 relevant studies focusing on social-ecological systems within agricultural or forestry domains. Results reveal that participation was mostly carried out at lower spatial levels (local and regional) while stakeholders commonly originated from one spatial level, mainly the study area. Agent behavior was mainly simulated at a single spatial level. The number of studies modeling multi- and cross-scale levels or integrating participation was small. In rare cross-level modeling cases, local-level agents were predominately simulated as an aggregate at higher levels. We found that stakeholders most commonly participated in model parameterization and calibration, but rarely in model results communication. Participation was also relatively strong in model design, development and validation. This can be explained by the need of scientists for model input or to improve the transparency of the modeling process. To address these gaps in future studies, we recommend to model agents operating at their “real-world” level, while capturing interactions between agents across different scales, rather than (dis)aggregate agent behaviors to a single level. Furthermore, participation should go beyond fulfilling the mere scientific requirement of model development, and should be used to enhance the applicability and relevance of SES models.

Is there a need for a CT scan of the pancreatic phase? A perfusion and simulation study of the pancreas, an HCC, and the kidney cortex.

To explore the peak enhancement time of a hepatocellular carcinoma, the pancreas, and the kidney cortex and its determinants.We obtained a time enhancement curve from the perfusion CT scans of 11 advanced HCC patients (40 volumes at 1.25 s time interval, slab slice 90 mm, bolus of 50 ml of iodinated contrast agent, 350 g iodine/ml, flow 5 ml/s). Small regions of interest were drawn on the abdominal aorta, the HCC, the cortex of the right kidney, and on the pancreas. The behavior of the contrast agent in the capillary and in the surrounding tissue was further explored with a finite element model.The peak enhancement time of the pancreas did not differ from that of the HCC (10±3 vs. 11±4 s, p=0.9), while the peak enhancement time of the kidney tended to be a few seconds earlier (8±1 s, p=0.082 vs. pancreas and p=0.069 vs. kidney). Simulation showed that the time span in which the tissue enhancement remained within 10% of its peak value was similar across all capillary densities and ranged between 26-38 s for a capillary density of 0.00125 per mm to 30-60 s for a capillary density of 0.01.The plateau tissue enhancement clinically acquired in the "late arterial phase" should be adequate both for the detection of hypervascular liver lesions such as HCCs and for obtaining peak pancreatic enhancement to detect hypovascular lesions. · The peak tissue enhancement time of an HCC, the pancreas, and the kidney cortex is similar. · The tissue peak enhancement time in the arterial phase is at the end of bolus transit. · Simulation shows that tissue enhancement peak time is a function of capillary density. · Cressoni M, Cadringher P, Colarieti A et al. Is there a need for a CT scan of the pancreatic phase? A perfusion and simulation study of the pancreas, an HCC, and the kidney cortex. Rofo 2025; DOI 10.1055/a-2516-3176.

The microvelocity of money in Ethereum

The transfer velocity of money is a macroeconomic quantity that measures the frequency of exchanges in an economy. For cryptoassets it can be exactly measured adopting a new approach, MicroVelocity. In this study we apply the framework to Ether, the native cryptocurrency of the Ethereum blockchain, to investigate velocity and its top contributors and how they can be characterised in the Ethereum ecosystem. While the inequalities and heterogeneity in wealth are well known, we here find that the same inequalities occur as well for MicroVelocity distribution and that this inequality is not explained just by wealth, but rather by the behaviour and economic activity of each individual agent.

Optimal Incentive Contracts With Bonus Caps

ABSTRACTThis article investigates contracts between two risk‐neutral parties with bounded bonus payments. If the available signal about the agent's behavior satisfies a novel condition, the monotone likelihood ratio transformation property, the optimal contract takes a simple form irrespective of whether the first‐order approach (FOA) is valid or not. The contract rewards the agent the maximum bonus if the signal's likelihood ratio exceeds a threshold, which in contrast to the FOA contract is not necessarily zero. We next derive a condition for a signal to enhance the efficiency of a contract. Applications in relational contracting and law enforcement illustrate our findings.

Robust Resilient Base Containment Control of Fractional Order Multiagent Systems With Disturbance and Time Delays

ABSTRACTThis article examines the resilient base containment control (CC) for fractional order multiple agent system with disturbance term in the dynamical system. In order to deal with resilient base CC in Riemann–Liouville sense with time delays, a simple and effective method is proposed where followers have weighted digraph topology among them. A disturbance factor is present into both linear and nonlinear delayed RL‐fractional‐order multiple agent systems (FMASs) with distributed time delays. This disturbance term effect the agents by introducing the uncertainty into the behavior of agents. In this article, Lyapunov technique, fractional calculus properties, and analytical methods are used to approach resilient base CC. The major benefit of this approach is that the first‐order derivative of Lyapunov function may be used to keep away from complex calculations, which effectively reduce troubles and difficulties resulting from time delays and fractional derivatives. At the end of our study, three illustration are provided to show the effectiveness and validity of our approach.

Who is to blame for the ‘problem’ of teenage pregnancy? Narratives of blame in two South African communities

BackgroundThe framing of teenage pregnancy in social discourse influences the way in which adolescent girls and young women (AGYW) are treated, the extent to which they are supported, and to which they are able to engage with services and enact pregnancy prevention behaviours.MethodsThrough the analysis of data from a qualitative study conducted in the South African communities of Newcastle in KwaZulu-Natal and Moretele in the North-West province, we explored narratives of blame for teenage pregnancy. Data derived from seventy-one in-depth interviews conducted with forty AGYW aged between 15 and 24, twenty-four parents/caregivers of AGYW, five service providers, ten school educators, and two other community members. Interpretation of data drew upon blame, attribution and framing theories.FindingsThe overall framing of teenage pregnancy was overwhelmingly negative, with participants labelling it immoral and ‘wrong’. Findings were arranged into key thematic areas that emerged in the data relating to blame for teenage pregnancy: the individual centred ‘wrong-girl’ and developmental discourses, in which blame was apportioned to AGYW for being immature, reckless, irresponsible, disobedient and greedy; the ‘wrong-family’ discourse in which blame was attributed to parents of AGYW for poor parenting and failing to adequately care for the adolescents in their responsibility; the ‘wrong-men’ discourse in which blame was attributed to men for luring AGYW into transactional sex, and for perpetrating gender-based violence; and lastly the ‘wrong-society’ discourse which attributed blame to contextual/structural factors such as poverty, a high volume of unregulated drinking establishments, and a lack of youth-friendly services.ConclusionsPolicies, interventions and programmes focusing on adolescents’ sexual and reproductive health need to carefully consider the framing and narratives of blame and responsibility. There is an urgent need to shift away from the individualised moralistic shaming of pregnant AGYW, towards a recognition of a complex interplay of multilevel factors that enable or constrain AGYW’s agency. Policies and programmes should focus on both providing sufficient support to AGYW, while also addressing structural factors and harmful narratives and thus create the conditions necessary to enable AGYW to enact safe, informed, agentic and responsible decisions and behaviours about their own sexual and reproductive health.

Reinforcement Learning in Dynamic Grids: A Modified Q-Learning Approach to Autonomous Driving with Moving Obstacles

This paper presents an enhanced Q-learning framework for autonomous self-driving agents navigating dynamic grid-based environments. The proposed method addresses key challenges in autonomous navigation, such as real-time decision-making, obstacle avoidance, and efficient path planning in environments with static and dynamic obstacles. Unlike traditional approaches, this framework incorporates moving obstacles with randomized or predefined patterns, simulating real-world scenarios like pedestrian movements or other vehicles on the road. A modified reward mechanism is introduced, heavily penalizing collisions while incentivizing efficient and safe navigation toward the goal. The agent is trained using reinforcement learning principles, where its policy evolves through exploration and exploitation strategies, ensuring adaptability to complex environments. The framework also features real-time visualization, offering an intuitive representation of agent behaviours, obstacle dynamics, and learning progression. The experimental findings demonstrate considerable gains in convergence speed, obstacle avoidance efficiency, and flexibility when compared to the baseline Q-learning techniques. This study emphasises the potential of Q-learning in dynamic, developing contexts, opening the door for its use in real-world autonomous systems like as robots and self-driving automobiles.. Keywords: Q-learning, Reinforcement Learning, Dynamic Grids and Autonomous Driving

A Multidimensional Approach for Formal Modeling and Analyzing Medical Cyber-Physical Systems

The combination of integrated software controlling devices, networking capabilities, and sensing/actuation technologies in Medical Cyber-Physical Systems (M-CPS) highlights some specific research challenges. The major challenge is to formally ensure the confidentiality of the data or resources they handle. This study tackles this problem by proposing a formal approach that combines CA-BRS (Control Agent and Bigraphical Reactive Systems) and BPMN (Business Process Model Notation) to specify and analyze CPS in general, while respecting several dimensions. The structural dimension of the CPS, representing the space (physical and cyber entities) in which agents exist and interact, is defined with BRS. Control agents constitute the virtual dimension and observe and control the physical and cyber entities of their environment. The complex and adaptive behavior of CPS (behavioral dimension) is defined through several types of rules, each managing a possible evolution of a CPS component (physical, cyber, or virtual). Two distinctive perspectives are associated with the semantic interpretation of these rules: the states perspective and the activities perspective. This study focuses on the activities perspective that specifies the behavior of control agents with a BPMN activity diagram. This highlights how these two models (CA-BRS and BPMN) complement each other to assist the designer in defining formal models for CPS. Additionally, it reveals how to provide the CA-BRS model with means to control unauthorized access to an electronic health record system.

As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference

Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance. Crucially, a collective of active inference agents can, if they maintain a group-level Markov blanket, constitute a larger group-level active inference agent with a generative model of its own. This potential for computational scale-free structures speaks to the application of active inference to self-organizing systems across spatiotemporal scales, from cells to human collectives. Due to the difficulty of reconstructing the generative model that explains the behaviour of emergent group-level agents, there has been little research on this kind of multi-scale active inference. Here, we propose a data-driven methodology for characterising the relation between the generative model of a group-level agent and the dynamics of its constituent individual agents. We apply methods from computational cognitive modelling and computational psychiatry, applicable for active inference as well as other types of modelling approaches. Using a simple Multi-Armed Bandit task as an example, we employ the new ActiveInference.jl library for Julia to simulate a collective of agents who are equipped with a Markov blanket. We use sampling-based parameter estimation to make inferences about the generative model of the group-level agent, and we show that there is a non-trivial relationship between the generative models of individual agents and the group-level agent they constitute, even in this simple setting. Finally, we point to a number of ways in which this methodology might be applied to better understand the relations between nested active inference agents across scales.

Dissipation of sarin, soman, and sulfur mustard from various types of crushed concrete.

The behavior of chemical warfare agents (CWAs) on urban materials, such as concrete, significantly impacts forensic and military responses to chemical incidents. This study examined the persistence and degradation mechanisms of sarin (GB), soman (GD), and sulfur mustard (HD) on three types of commonly used concrete with varying water-cement ratios. Over two months, we evaluated the effects of concrete composition, temperature, and fragment size on CWA behavior. Half-lives and activation energies for CWA dissipation were calculated under various conditions. Results showed that concrete properties and external temperature strongly influenced dissipation rates. G-series agents underwent rapid hydrolysis, forming methylphosphonates, while HD degradation involved elimination, nucleophilic substitution, and oxidation, producing several previously unreported byproducts. Smaller concrete fragments increased recovery values and accelerated degradation due to greater surface area exposure, and higher temperatures further enhanced dissipation rates, particularly for volatile agents. Differences in dissipation among concrete types were linked to their physical and chemical properties, notably water-cement ratios. This study highlights the challenges of detecting CWAs due to their rapid penetration and transformation in concrete and provides insights for improving sampling, identification, and decontamination strategies under realistic conditions.

Spatial reciprocity under reinforcement learning mechanism.

At present, the research on the dynamics of cooperative behavior of agents under reinforcement learning mechanism either assumes that agents have global interaction, that is, agents interact with all other agents in the population, or directly study the influence of relevant factors on cooperation evolution based on the local interaction in a network structure. It neglects to formally study how the limitation of agents that only interact with local agents affects their strategy choice. Thus, in this paper, we study the cooperative behavior of agents in a typical social decision-making environment with conflicts between individual interests and collective interests. On the one hand, a programmed game model in game theory, namely, prisoner's dilemma game, is used to capture the essence of real-world dilemmas. On the other hand, the effects of local and global strategy learning on the cooperative evolution of agents are investigated separately, and the nature of spatial reciprocity under the reinforcement learning mechanism is found. Specifically, when there is no inherent connection between the interacting agents and the learning agents within the system, the network structure has a limited effect on promoting cooperation. It is only when there is an overlap between the interacting agents and the learning agents that the spatial reciprocity effect observed in the traditional evolutionary game theory can be fully realized.

Emergence of flocking behaviors transferring previously evolved alignment robot controllers

Flocking is a crucial collective behavior in swarm robotics. Reynolds introduced the boids model as a means to imitate flocking behaviors in artificial agents. This model relies on three fundamental local rules: separation, cohesion, and alignment. This paper examines the development of flocking behaviors only through the evolution of the alignment rule. Initially, we employ a genetic algorithm to develop the alignment behavior inside a group of stationary robots. The advanced alignment robot controller is a continuous-time recurrent neural network (CTRNN). Afterwards, we include the developed controller into a three-layered subsumption architecture in order to accomplish flocking behavior. Aside from the advanced alignment behavior, the architecture also incorporates a rudimentary manually designed obstacle avoidance behavior and a subroutine for moving forward. The initial experiment centers on the progression of alignment among the robots. Advanced communication techniques result in a scalable and precise alignment, where both the message content and its related context are very pertinent. The second experiment investigates the development of flocking behavior. The results indicate that the suggested subsumption architecture is capable of achieving efficient flocking behaviors. In addition, the robot swarm has the ability to navigate around barriers and continue to exhibit flocking behavior once the impediments have been bypassed. Our research indicates that the formation of a cohesive group can occur by implementing a single developed rule, complemented with well designed actions for avoiding obstacles and navigating the environment.

Agentic but not Aggressive: Assessing the State of Televised Debate Styles in Latin America

This study assesses the state of political candidates’ verbal and nonverbal expressive behaviors during televised debates in four Latin American countries (Mexico, Colombia, Peru, and Argentina), a region largely overlooked in debate studies where uncivil behavior driven by populism and related ideologies is on the rise. Utilizing human annotation for both descriptive analysis and training and validation of machine learning classifiers, we analyzed 900 ten-second segments from recent debates in each country to detect behaviors associated with a populist style, including adversarial rhetoric and verbal attacks, an aggressive or threatening tone, angry facial expressions, and defiant gestures. We also identified agentic behaviors characterized by assertive, dynamic, and physically energetic actions, lacking the aggressive or threatening markers. Findings reveal distinct communication styles across countries coalescing around a core tendency. While aggressive and adversarial behaviors were anticipated, the predominant style observed was agentic, characterized by assertive and dynamic gestures absent displays of anger, defiance, or hostility. Agentic behavior was common despite the diversity in debate formats and party systems. The study underscores the importance of multimodal analysis in understanding political communication cross-nationally and provides insights into the political styles and performative strategies employed by Latin American candidates in the countries observed.

The Application of Intelligent Agent-Based Model Method in the Operation Process of the Catering Industry

The Intelligent Agent-Based Model method, by modeling many interacting individuals and simulating the behavior of individual agents, demonstrates the operation of complex multi-agent systems from a micro to a macro level. It can be effectively utilized to study the operational process of the catering industry surrounding rural scenic spots. This study takes the catering industry surrounding a tourism scenic spot in the southern countryside of Guizhou, China as an example. The emergence and development process of the catering industry is analyzed and the interaction process between tourists and restaurants is modeled. The consistency between the simulation results of restaurant customer flow and the field survey findings indicates that this model could be used to simulate the actual operation of the restaurant. The simulations shows that when the tourist traffic is high, the Matthew Effect on the distribution of customer flow among restaurants is not significant, and the impact of restaurant location is limited. However, when the tourist traffic at the scenic spot is low such as during the off-peak season, the location of restaurants has great influence on the customer flow. Furthermore, establishing partnerships with travel agencies represents an alternative solution for restaurants.

How low education increases COVID-19 mortality: the mediating role of vaccination, distrust in science, and lack of preventative health behavior.

Previous research shows the significant correlation between low education and COVID-19 mortality in underprivileged communities, even when accounting for factors like poverty and race. The exact mechanisms by which low education gives rise to COVID-19 mortality, however, are less clear. We propose that low education predicts COVID-19 morality because low education gives rise to a less engaged, less agentic approach to one's own healthcare. We operationalize low engagement and low agentic behavior as four variables that mediate the effect of low education on COVID-19 mortality: (i) vaccination, (ii) distrust of science (Republican vote), (iii) poor health, and (iv) prevention. We model COVID-19 mortality in 3108 counties of the United States, using deaths across 60 fortnights. All four indicators of an agentic, engaged approach to health are statistically significant mediators of the relationship between low education and COVID-19 mortality: vaccination [IRR=1.02; (1.02, 1.03)]; Republican vote [IRR: 1.07; (1.06, 1.09)]; poor health [IRR: 1.01; (1.01, 1.02)]; and prevention [IRR: 1.00, (1.001, 1.003)]. These findings suggest that low level of formal education predicts mortality from COVID-19 because low levels of education gives rise to a less engaged and less agentic approach to one's own health.

Development and validation of the online agentic and communal behaviour scale: implications for emotional well-being

ABSTRACT Agentic behaviours are often characterised as actions that revolve around being assertive and achievement-oriented, whereas communal behaviours are centered on cooperation, empathy, and promoting social harmony. While extensive studies have demonstrated how these behaviours can be reproduced in online environments from offline, there is a lack of a suitable scale to measure them. The current study focuses on developing and validating an online agentic and communal behavioural survey scale. Through exploratory and confirmatory factor analyses, two correlated factors emerged and corresponded with the definitions of the agentic and communal dimensions. Through linear regression modelling, it was found that although each dimension was negatively associated with Internet users’ emotional well-being, the positive impact of online communal behaviour on emotional well-being is more pronounced at higher levels of online agentic behaviour. This reflects a theoretical perspective that both dimensions are complementary and should be ‘balanced’ to achieve optimal well-being. In a three-way interaction, it was found that gender moderates the above positive interaction effect in both gender groups. Specifically, gender differences play a part in determining the impact of high vs. low online AC behaviours on emotional well-being. This finding aligns with the theoretical framework explaining gender roles in societies.

Optimizing feedback reception: a scoping review of skills and strategies for medical learners

Background: Feedback remains essential to a learner’s professional development. Most feedback literature focuses on provision of feedback, and there is a lack of evidence-based data to support learners in developing skills to receive, evaluate and use feedback, independently of context. This scoping review mapped the literature regarding strategies and skills that optimize medical learners’ reception to feedback. Methods: Investigators conducted searches in MEDLINE, Embase, ERIC, APA PsycINFO and Web of Science Core collection from inception to May 2023. Study inclusion criteria were primary evidence sources, and strategies or skills for improved feedback reception for medical learners. Data were screened and extracted by pairs of independent reviewers. Investigators summarized study characteristics, outcomes, educational methods, and interventions. Results: Of 7692 total studies, six provided strategies and skills to improve feedback reception. Delivery of education was via workshops (n = 5 studies) that proposed cognitive, reflective and experiential learning activities, all reporting learners’ self-perceived improvement of feedback behaviour. Nine strategies and seven tools were identified, focusing on general approach, soliciting or evaluating feedback. Conclusion: The six included studies outline nine strategies and seven skills for improved learner feedback reception, focusing on overall approach and agentic behaviours without evaluation of the strategies or skills in practice. Key concepts and gaps in the literature were identified and may guide further investigation to optimize learner reception to feedback.

РАЗВИТИЕ ПОЗНАВАТЕЛЬНОЙ АКТИВНОСТИ СТУДЕНТОВ КОЛЛЕДЖА С ИСПОЛЬЗОВАНИЕМ ИНТЕРАКТИВНЫХ МЕТОДОВ ОБУЧЕНИЯ ПРИ ИЗУЧЕНИИ ЭКОНОМИЧЕСКИХ ДИСЦИПЛИН

Economic disciplines, being the sciences of the behavior of economic agents, focus on the analysis of the production and distribution of limited resources in order to maximize public welfare — this field of knowledge intersects with other disciplines (sociology, political science, psychology), however, economic analysis uses unique methodological approaches focused on quantitative assessments, modeling and forecasting.

Объяснимость поведения агентов в системах поддержки принятия клинических решений

The article considers the task of improving the explainability of agent behavior in clinical decision support systems using multi-agent reinforcement learning, taking into account the behavioral characteristics of individual agents. Special attention is paid to the application of the proximal policy optimization (PPO) algorithm, which is used to simulate the interaction of artificial and natural intelligence agents. In addition, the importance of taking into account the behavior-al characteristics of patients is considered, which is achieved through the use of the developed framework «beliefs-desires-intentions» (BDI). The framework allows you to model patient behavior more accurately, taking into account their individual preferences and needs, which contributes to improving the quality of medical care. To increase the explainability of agent behavior and ensure transparency of decisions, the use of a large language model (LLM) is pro-posed. To assess the quality of the framework as a means of ensuring the requirements of XAI (explicable artificial intelligence), a set of criteria has been developed and its representativeness has been demonstrated. The assessment was carried out by comparing the explanations generated by four popular language models for seven gameplay launches with different initial data. The proposed approach to ensuring explainability in multi-agent simulations opens up opportunities for further development and implementation of such systems in clinical practice.

Selection оf energy sources for prosumers in the centralized heat supply system using agent technologies

In recent years, the energy sector has been widely developing the direction associated with the introduction of distributed energy generation and the emergence of prosumers (PR), including within the heat supply system. Due to the emergence of consumers as participants in energy markets who take an active part in the process of managing their energy supply, there is a need to make decisions on energy supply options in the context of conflicting interests of the parties - the PR and the centralized heat supply system (CHS). The article presents a mathematical formulation of the problem of finding a compromise solution, which includes an PR model, a CHS model and a generalized desirability criterion. A methodology for selecting energy source equipment for PR in the CHS has been developed, taking into account the interests of the CHS and PR and their ability to produce heat energy. A structure of a multi-agent system is proposed and agent behavior algorithms are developed that take into account the complex behavior of the PR and CHS objects. The results of testing the developed methodological and software on a test model of the heat supply system are presented.