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Exploration in sequential decision problems is a computationally challenging problem. Yet, animals exhibit effective exploration strategies, discovering shortcuts and efficient routes toward rewarding sites. Characterizing this efficiency in animal exploration is an important goal in many areas of research, from ecology to psychology and neuroscience to machine learning. In this study, we aim to understand the exploration behavior of animals freely navigating a complex maze with many decision points. We propose an algorithm based on a few simple principles of animal movement from foraging studies in ecology and formalized using reinforcement learning. Our approach not only captures the search efficiency and turning biases of real animals but also uncovers longer spatial and temporal dependencies in the decisions of animals during their exploration of the maze. Through this work, we aspire to unveil a novel approach in cognitive science of drawing interdisciplinary inspiration to advancing the field's understanding of complex decision-making.

Science can be viewed as a collective, epistemic endeavor. However, a variety of factors- such as the publish-or-perish culture, institutional incentives, and publishers who favor novel and positive findings- may challenge the ability of science to accurately aggregate information about the world. Evidence of the shortcomings in the current structure of science can be seen in the replication crisis that faces psychology and other disciplines. We analyze scientific publishing through the lens of cultural evolution, framing the scientific process as a multi-generational interplay between scientists and publishers in a multi-armed bandit setting. We examine the dynamics of this model through simulations, exploring the effect that different publication policies have on the accuracy of the published scientific record. Our findings highlight the need for replications and caution against behaviors that prioritize factors uncorrelated with result accuracy.

An agent-based model for semantic search and retrieval in memory is proposed. The model seeks to generate verbal fluency lists with properties similar to those generated by humans in the semantic fluency task. This model is compared to a random walk in a semantic network in its ability to adjust to the results of 141 undergraduate students in the semantic fluency task in eight different outcomes. We found that the agent-based model fits participants' results better than the random walk model. The results were consistent with optimal foraging theories, and the distributions of the total number of words, similarities, and frequency values were similar to those generated by participants. The potential uses of this model as a virtual environment to experiment with the search and retrieval process in semantic memory are discussed.

Social rewards shape human behavior. During development, a caregiver guides a learner's behavior towards culturally aligned goals and values. How do these behaviors persist and generalize when the caregiver is no longer present, and the learner must continue autonomously? Here, we propose a model of value internalization where social feedback trains an internal social reward (ISR) model that generates internal rewards when social rewards are unavailable. Through empirical simulations, we show that an ISR model prevents agents from unlearning socialized behaviors and enables generalization in out-of-distribution tasks. Incomplete internalization, akin to "reward hacking" on the ISR, is observed when the model is undertrained. Finally, we show that our model internalizes prosocial behavior in a multi-agent environment. Our work provides a framework for understanding how humans acquire and generalize values and offers insights for aligning AI with human values.

Recent years have seen an explosion of theoretical interest, as well as increasingly fraught real-world debate, around issues to do with discourse participation. For example, marginalised groups may find themselves excluded or may exclude themselves from discourse contexts that are hostile. This not only has ethical implications, but likely impacts epistemic outcomes. The nature and scale of such outcomes remain difficult to estimate in practice. In this paper, we use agent-based modelling to explore the implications of a tendency toward `agreeableness' whereby agents might shape their communication so as to reduce direct conflict. Our simulations show that even mild tendencies to avoid disagreement can have significant consequences for information exchange and the resultant beliefs within a population.

Contemporary formal models aim to capture group polarization as the result of deliberation between rational agents. Paradigmatic models do, however, rely on rather limited agents, casting doubt on the conclusion that group polarization can be rationally reconstructed. In this paper, we use a recently developed Bayesian agent-based model of deliberation to investigate this conclusion. This model avoids problems we identify in a group of influential Bayesian polarization models. Our case study shows that a simple mechanism produces realistic patterns of group polarization: limited exchange of evidence across a sparse social network. We reflect on what our results mean for our formal understanding of rational group polarization.

We investigate the phenomenon of colexification, where a sin-gle wordform is associated with multiple meanings. Previ-ous research on colexification has primarily focused on em-pirical studies of different properties of the meanings that de-termine colexification, such as semantic similarity or meaningfrequency. Meanwhile, little attention was paid to the word-forms' properties, despite being the original approach advo-cated by Zipf. Our preregistered study examines whether wordlength influences word choice for colexification using a noveldyadic communication game (N = 64) and a computationalmodel grounded in the Rational Speech Act (RSA) framework.Contrary to initial predictions, participants did not exhibit astrong preference for efficient colexification (namely colexi-fying multiple concepts using short words, when long alter-natives are available). The results align more closely with asimpler coordination model, where dyads align on a function-ing lexical convention with relatively little influence from theefficiency of that convention. Our study highlights the pos-sibility that colexification choices are strongly determined bythe pressure for coordination, with weaker influences from se-mantic similarity or meaning frequency. This is most likelyexplained by weak pressure for efficiency in our experimentaldesign.

Social norms are often characterized as a system of rules that guide behavior. However, social norms also allow for flexibility; not entirely restricting individuals to one possible behavior. Here, we put forward an agent-based cognitive model that captures social norms as processes that are socially constructed through interactions between individuals. In this modelling work, we focus on the role of norm acquisition and conformity bias in both action production and inference-making. This computational cognitive model allows us to think about social norms along three dimensions: individual vs. collective, behavior vs. belief, and subjective vs. objective. Our simulation results show that increased conformity bias can induce misjudgments about the true desires of others and misalignment between different agents' perceptions of the social norm. However, if agents do not assume that others also conform in their behavior, this increased conformity bias does not necessarily lead to excessive misperceptions of the social norm.

In human social interactions, decisions are naturally influenced by both individual needs and the needs of others. However, it remains unclear whether cognitive robots exhibit similar needs-guided decision-making characteristics. In this study, we design a collaborative tracking task to evaluate this phenomenon. Specifically, we develop a needs-guided reinforcement learning framework that enables robots to autonomously learn and shape behavior by considering both their intrinsic needs and those of others. Our experiments highlight that the robots' inherent needs play a more crucial role in decision-making than the needs of others. In essence, our model establishes an interpretable foundation for applications in cognitive robotics.

Working memory is widely assumed to underlie multi-step planning, where representations of possible future actions and rewards are iteratively updated before determining a choice. But most working memory research focuses on a context where stimuli are presented simultaneously and the value of encoding each stimulus is independent of others. It is unclear how working memory functions in planning scenarios where the rewards of future actions unfold over time, are retained in working memory, and must be integrated for plan selection. To bridge this gap, we adapted a version of the "mouselab task" in which participants sequentially observe the reward at each node in a decision tree before selecting a plan that maximizes cumulative rewards. We specified a theoretical model to characterize the optimal encoding and maintenance strategy for this task given the working memory constraints, which trades off the cost of storing information with the potential benefit of informing later choices. The model encoded rewards in choice-relevant plans more often, in particular, rewards on the best and (to a lesser extent) worst plans. We then tested this hypotheses on human participants, who showed the same pattern in the accuracy of their explicit recall. Our study thus establishes an empirical and theoretical foundation for models of how people encode and maintain information during planning.

Human moral judgments are both precise, with clear intuitions about right and wrong, and at the same time obscure, as they seem to result from principles whose logic often escapes us. The development of Artificial Intelligence (AI) applications requires an understanding of this subtle logic if we are to embed moral considerations in artificial systems. Reinforcement Learning (RL) algorithms have emerged as a valuable interactive tool for investigating moral behavior. However, being value-based algorithms, they face difficulty when it comes to explaining deontological, non-consequentialist moral judgments. Here, in a multi-agent learning scenario based on the Producer-Scrounger Game, we show that RL agents can converge towards apparently non-consequentialist outcomes, provided the algorithm accounts for the temporal value of actions. The implications of our findings extend to integrating morality into AI agents by elucidating the interplay between learning strategies, characteristics for accounting temporal values, and methods of considering the opponent's payoff.

Theory of mind is an essential ability for complex social interaction and collaboration. Researchers in cognitive science and psychology have previously sought to integrate theory of mind capabilities into artificial intelligence (AI) agents to improve collaborative abilities (Cuzzolin, Morelli, Cirstea, & Sarahakian, 2020). We introduce the Recurrent Conditional Variational Autoencoder (RCVAE), a novel model which leverages the ability of generative models to learn rich abstracted representations of contextual behaviors to predict behavioral intent from human behavioral trajectories. Advancing on current concept learning models, this model allows for the discovery of latent intent in human behavior trajectories, while maintaining the scalability and performance of generative AI models. We show that in the Overcooked-AI environment, the RCVAE outperforms baseline Long Short-Term Memory (LSTM) models in predicting intent, achieving higher prediction accuracy and greater predictive stability. The implications of these results are significant; the RCVAE's proficiency in learning the relationship between basic actions and resulting contextual behaviors represents a significant advancement in concept learning for behavioral intent prediction.

Prior literature shows that some mechanisms, e.g., commitment, could give rise to cooperation. However, participants' diverse propensities to cooperate may limit such mechanisms' effectiveness. Thus, we bring individual differences in their propensities to cooperate into the reasoning of long-term social dynamics of cooperation through an agent-based modeling approach. Our results suggest that commitment may still guarantee cooperation when individuals have different propensities to cooperate but has weaker effects, and the setups of commitment are also important. Our study highlights the importance of integrating individual preferences in analyzing collective dynamics of a population consisting of individuals of heterogeneous characteristics, thus offering implications to facilitate cooperation in rich real-world scenarios.

Large Language models (LLM) exhibit human-like proficiency in various tasks such as translation, question answering, essay writing, and programming. Emerging research explores the use of LLMs in collective problem-solving endeavors, such as tasks where groups try to uncover clues through discussions. Although prior work has investigated individual problem-solving tasks, leveraging LLM-powered agents for group consensus and decision-making remains largely unexplored. This research addresses this gap by (1) proposing an algorithm to enable free-form conversation in groups of LLM agents, (2) creating metrics to evaluate the human-likeness of the generated dialogue and problem-solving performance, and (3) evaluating LLM agent groups against human groups using an open source dataset. Our results reveal that LLM groups outperform human groups in problem-solving tasks. LLM groups also show a greater improvement in scores after participating in free discussions. In particular, analyses indicate that LLM agent groups exhibit more disagreements, complex statements, and a propensity for positive statements compared to human groups. The results shed light on the potential of LLMs to facilitate collective reasoning and provide insight into the dynamics of group interactions involving synthetic LLM agents.

This paper presents an approach to model the internal cognitive state of decision-makers when interacting with AI to understand exchanges between agents and improve future interactions. We focus on understanding how AI suggestions are perceived by a human agent using an approach based on the technology acceptance model. The variation in the user's state is investigated when perceiving the interaction with AI by considering it as a hidden (latent) state. Using human evaluation data collected from two cases of clinical decision-making and software development scenarios, we analyse and explore the user's perceptional state during interaction. The experiment conducted employs the Bayesian belief network to represent the human perceptional model and provide a prediction of the usefulness of AI model's suggestions in the considered case. Upon introduction of cognitive states in the model, we observed an increase in predictive performance by 76–77%. Our investigation can be concluded as an attempt to identify implicit static and dynamic cognitive characteristics of users to provide personalized assistance in human-AI interaction (HAI) and collaboration in complex domains of decision-making

Artificial neural networks trained using deep-learning methods to solve a simple reference game by optimizing a task-specific utility develop efficient semantic categorization systems that trade off complexity against informativeness, much like the category systems of human languages do. But what exact type of structures in the semantic space could result in efficient categories, and how are these structures shaped by the contexts of communication? We propose a NN model that moves beyond the minimal dyadic setup and show that the emergence of convexity, a property of semantic systems that facilitates this efficiency, is dependent on the amount of variability in communication contexts across partners. We use a method of input representation based on compositional vector embeddings that is able to achieve a higher level of communication success than regular non-compositional representation methods, and can achieve a better balance between maintaining the structure of the semantic space and optimizing utility.

How does the brain control attention? The Attention Schema Theory suggests that the brain explicitly models its state of attention, termed an attention schema, for its control. However, it remains unclear under which circumstances an attention schema is computationally useful, and whether it can emerge in a learning system without hard-wiring. To address these questions, we trained a reinforcement learning agent with attention to track and catch a ball in a noisy environment. Crucially, the agent had additional resources that it could freely use. We asked under which conditions these additional resources develop an attention schema to track attention. We found that the more uncertain the agent was about the location of its attentional window, the more it benefited from these additional resources, which developed an attention schema. Together, these results indicate that an attention schema emerges in simple learning systems where attention is important and difficult to track.

Opinion averaging is a common means of judgment aggregation that is employed in the service of crowd wisdom effects. In this paper, we use simulations with agent-based models to highlight contexts in which opinion averaging leads to poor outcomes. Specifically, we illustrate the conditions under which the optimal posterior prescribed by a normative model of Bayesian argument exchange diverges from the mean belief that would be arrived at via simple averaging. The theoretical and practical implications of this are discussed.

Several recent contributions to the research on group problem solving suggest that reducing the connectivity between agents in a social network may be epistemically beneficial. This notion stems from the idea that collective problem-solving behavior may benefit from the transient diversity in agents' beliefs due to increased individual exploration and decreased social influence. At the same time, however, lower connectivity hinders the diffusion of good solutions between network members. Our simulation findings shed light on this trade-off. We identify conditions under which the less-is-more effect is likely to manifest. Our findings suggest that a community consisting of semi-isolated groups could provide an answer to the tension between diversity and diffusion.

People frequently encounter the challenge of deciding when to stop exploring options to optimize outcomes, such as when selecting an apartment in a fluctuating housing market or booking a dinner reservation on New Year's Eve. Despite experiencing these decisions on multiple occasions, people often struggle to stop searching optimally. This research investigates human learning abilities in optimal stopping tasks, focusing on feedback and knowledge of option value distributions. Through an experimental sequential choice task, we demonstrate that experience improves performance, with feedback significantly influencing learning. We also find that awareness of the value distribution reduces the duration of the search. A cognitive model accurately predicts these effects, shedding light on human learning processes.

In hierarchical perception-action (PA) learning, agents discover invariants between percepts and actions that are structured hierarchically, from very basic immediate links to higher-level, more abstract notions. In practice, existing work tends to either focus on the general theory at the expense of details of the proposed mechanisms, or specify a-priori the contents of some layers. Here, we introduce a framework that does without such constraints. We demonstrate the framework in a simple 2D environment using an agent that has minimal perceptual and action abilities. We vary the perceptual abilities of the agent to explore how the specifics of this aspect of the agent's body might affect PA learning and find unexpected consequences. The contribution of this paper is therefore twofold, (1) we add a novel framework to the literature on PA learning, using, in particular curiosity-based reinforcement learning (RL) to implement the necessary learning mechanisms, and (2) we demonstrate that even for very simple agents, the relation between the specifics of an agent's body and its cognitive abilities is not straightforward.

Conformity refers to phenomena where people match their behavior to others. Much research has focused on cases where people observe others in identical situations, saying little about its depth or generalizability. When conforming, do people revise behaviors only in that specific situation, or do they update more deeply to maintain consistent behaviors across situations? Using simulations, we first show that deep and shallow conformity leads to contrasting group dynamics; only with deep conformity can groups accumulate improvements beyond individual lifespans. We further conduct an experiment using an estimation task to examine the depths of conformity in humans. People generally extended conformist social influence to new situations without direct reference to others. However, those who simply averaged their answer with that of the direct reference showed notable failures in this generalization. Collectively, our research highlights the importance of distinguishing different depths of conformity when studying social influence and resulting group outcomes.

In this paper, we first introduce the `complex hidden profile', a previously overlooked category of hidden profiles that arises from complex inferential relations among arguments.Second, in order to investigate the conditions under which interrelated arguments can generate hidden profiles, we introduce a novel Bayesian agent-based framework for collective reasoning with complex argument structures.Finally, we show that that many possible argument structures can generate hidden profiles, even when agents do not have any information in common.

Political polarisation is growing along with its negative consequences – degradation of functional government and increases in stochastic violence. Polarisation can result from both cognitive factors affecting information processing and biased information ecosystems, but their interactions are poorly understood. We present an agent-based model combining a varyingly polarised media landscape with agents driven by homophily and uncertain (political) identity inference processes. Agents were motivated to find similar others to form an ingroup by comparing moral values expressed in response to environmentally imposed moral dilemmas. Media pushed moral values in line with either liberal or conservative values, varying in agreement and influence. Liberal agents were more satisfied (according to homophily motivations), formed larger, more stable clusters, and morally disengaged less than conservatives. Identity aligned media exposure increased liberal agents' satisfaction, but had no, or the opposite effect, on conservative agents. We conclude that media exposure asymmetrically affects political polarisation across political identities.

Various methods for social learning have been proposed within the reinforcement learning framework. These methods involve the social transmission of information within specific representational formats like policies, value, or world models. However, transmission of higher-level, model-based representations typically require costly inference (i.e., mentalizing) to ``unpack'' observable actions into putative mental states (e.g., with inverse reinforcement learning). Here, we investigate cheaper, non-mentalizing alternatives to social transmission of model-based representations that bias the statistics of experience to ``hijack'' asocial mechanisms for learning of environments. We simulate a spatial foraging task where a naïve learner learns alone or through observing a pre-trained expert. We test model-free vs. model-based learning together with simple non-mentalizing social learning strategies. Through analysis of generalization when the expert can no longer be observed and through correspondence between expert and learner representations, we show how simple social learning mechanisms can give rise to complex forms of cultural transmission.

The complexity of studying behavior and cognitive processes in realistic ecological tasks is a major challenge for cognitive scientists, behavioral ecologists, community ecologists, and the cognitive ecology community that subsumes all these fields. Here we describe a modeling approach that can be used to study the decision-making trade-offs that emerge from the coupling of nervous systems, bodies, and ecological context. To demonstrate the method, we describe an agent that must balance its need to consume resources with its need to avoid predation. We then show how to analyze the resulting behavior through the lens of behavioral trade-off schemas synthesized with neural traces measured during real-time behavior. The employment of model agents will be an important contributor to ecological theory of cognitive processes, and here we hope to convince the reader of that methodological potential.

Many recent works in the field of multi-agent reinforcementlearning via communication focus on learning what messagesto send, when to send, and whom to address such messages.Those works indicate that communication is useful for highercumulative reward or task success. However, one important limitation is that most of them ignore the importance of enforcingagents' ability to understand the received information. In thispaper, we notice that observation and communication signalsare from separate information sources. Thus, we enhance thecommunicating agents with the capability to integrate crucialinformation from different sources. Specifically, we propose amulti-modal communication method, which modulates agents'observation and communication signals as different modalitiesand performs multi-modal fusion to allow knowledge to transferacross different modalities. We evaluate the proposed methodon a diverse set of cooperative multi-agent tasks with severalstate-of-the-art algorithms. Results demonstrate the effectiveness of our method in incorporating knowledge and gaining adeeper understanding from various information sources.

Natural languages tend to minimize the linear distance between heads and their dependents in a sentence, known as dependency length minimization (DLM). Such a preference, however, has not been consistently replicated with neural agent simulations. Comparing the behavior of models with that of human learners can reveal which aspects affect the emergence of this phenomenon. This work investigates the minimal conditions that may lead neural learners to develop a DLM preference. We add three factors to the standard neural-agent language learning and communication framework to make the simulation more realistic, namely: (i) the presence of noise during listening, (ii) context-sensitivity of word use, and (iii) incremental sentence processing. While no preference appears in production, we show that the proposed factors contribute to a small but significant learning advantage of DLM for listeners of verb-initial languages. Our findings offer insights into essential elements contributing to DLM preferences in purely statistical learners.

Grief is a reaction to loss that is observed across human cultures and even in other species. While the particular expressions of grief vary significantly, universal aspects include experiences of emotional pain and frequent remembering of what was lost. Despite its prevalence, and its obvious nature, considering grief from a normative perspective is puzzling: Why do we grieve? Why is it painful? And why is it sometimes prolonged enough to be clinically impairing? Using the framework of reinforcement learning with memory replay, we offer answers to these questions and suggest, counter-intuitively, that grief may have normative value with respect to reward maximization. We additionally perform a set of simulations that identify and explore optimal grieving parameters, and use our model to account for empirical phenomena such as individual differences in human grief trajectories.

Attachment is crucial for infants' cognitive development and social relationships. Traditional attachment research has been qualitative, lacking a model to explain how infants' attachment styles develop from experience and how these are influenced by personal traits and environmental factors. We propose such a model, predicting how infants balance interaction with caregivers against exploring their surroundings. Our study is based in a grid-world environment containing an infant and caregiver agent. We vary the infant's temperamental factors (e.g., ability to regulate emotions and preferences for social vs. environmental reward), and caregiver behavior (whether positive or negative interactions are more likely). We find that different equilibria result that qualitatively correspond to different attachment styles. Our findings suggest that the characteristic exploratory behavior of each attachment style in real infants may arise from interactions of infant temperament and caregiver behaviors.

Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs.

Generalisation of a linguistic rule can be facilitated by certain distributional characteristics. Previous work has shown that a rule is better generalised if it applies to items that (i) follow a skewed frequency distribution, or (ii) follow a uniform frequency distribution over many distinct item types. These two observations cannot be unified under explanations of rule generalisation that are based on entropy of the frequency distributions (since skewed distributions have low entropy, while a greater type count increases the entropy), nor explanations that focus on one highly-frequent type providing a basis for analogical extension (since all types in uniform distributions are equally frequent). Using an artificial language learning experiment and an agent-based model, we show that participants' generalisation behaviour is best matched by a model encoding preferential generalisation of rules containing long-tailed distributions—that is, containing a greater number of low-frequency types.

INTRODUCTION: Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes. OBJECTIVES: The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate. METHODS: Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework. RESULTS AND DISCUSSION: The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change. CONCLUSION: This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.

BackgroundUnderstanding the impact of incarceration on HIV transmission among Black men who have sex with men is important given their disproportionate representation among people experiencing incarceration and the potential impact of incarceration on social and sexual networks, employment, housing, and medical care. We developed an agent-based network model (ABNM) of 10,000 agents representing young Black men who have sex with men in the city of Chicago to examine the impact of varying degrees of post-incarceration care disruption and care engagement interventions following release from jail on HIV incidence.MethodsExponential random graph models were used to model network formation and dissolution dynamics, and network dynamics and HIV care continuum engagement were varied according to incarceration status. Hypothetical interventions to improve post-release engagement in HIV care for individuals with incarceration (e.g., enhanced case management, linkage to housing and employment services) were compared to a control scenario with no change in HIV care engagement after release.FindingHIV incidence at 10 years was 4.98 [95% simulation interval (SI): 4.87, 5.09 per 100 person-years (py)] in the model population overall; 5.58 (95% SI 5.38, 5.76 per 100 py) among those with history of incarceration, and 12.86 (95% SI 11.89, 13.73 per 100 py) among partners of agents recently released from incarceration. Sustained post-release HIV care for agents with HIV and experiencing recent incarceration resulted in a 46% reduction in HIV incidence among post-incarceration partners [incidence rate (IR) per 100 py = 5.72 (95% SI 5.19, 6.27) vs. 10.61 (95% SI 10.09, 11.24); incidence rate ratio (IRR) = 0.54; (95% SI 0.48, 0.60)] and a 19% reduction in HIV incidence in the population overall [(IR per 100 py = 3.89 (95% SI 3.81-3.99) vs. 4.83 (95% SI 4.73, 4.92); IRR = 0.81 (95% SI 0.78, 0.83)] compared to a scenario with no change in HIV care engagement from pre-to post-release.InterpretationDeveloping effective and scalable interventions to increase HIV care engagement among individuals experiencing recent incarceration and their sexual partners is needed to reduce HIV transmission among Black men who have sex with men.FundingThis work was supported by the following grants from the National Institutes of Health: R01DA039934; P20 GM 130414; P30 AI 042853; P30MH058107; T32 DA 043469; U2C DA050098 and the California HIV/AIDS Research Program: OS17-LA-003; H21PC3466.

Industrial symbiosis (IS) facilitates the transition toward a circular built environment. Following IS principles, multiple buildings can be symbiotically linked via closed‐loop material flows beyond the boundaries of individual projects. However, there are few IS matchmaking methods that support the identification of IS opportunities among multiple deconstruction and construction projects. This research develops an agent‐based model to fill this gap. The agent architecture is designed based on the concept of shearing layers. Circularity hubs are proposed to support IS matchmaking by allowing larger transportation ranges and keeping IS requests active for longer periods. The model's applicability is demonstrated through an industrial–urban symbiosis case in Enschede, the Netherlands. The model simulates the spatial–temporal dynamics of IS matchmaking as an emergent phenomenon under future scenarios. The results show operational evidence of IS matchmaking via the strategic implementation of circularity hubs. Overall, this research provides a new methodological perspective to explore the circularity in the built environment at scale. [ABSTRACT FROM AUTHOR]

Background: Simulations, ludic or otherwise, have so far struggled to gain a foothold in mainstream historiography. Some authors suggest there may be fundamental incompatibilities between history and the language of simulations and scholarly games. Others believe that designing, employing, and validating historical simulations may be simply too costly and/or labor-intensive to justify their widespread adoption. Intervention: This paper intends to identify points of friction between historiography and simulation-based research and suggest practical solutions to these issues. Methods: My discussion is based on the description and analysis of a case study, the ThomondSim/ The Triumphs of Turlough research project. The initiative consisted of the development and application of an agent-based computational model (ABM) and a scholarly board game to investigate the possible associations between economic, environmental, and military hazards in 13th and 14th centuries Ireland. Results and Discussion: Ensuring the simulations matched historical evidence to a standard deemed acceptable by the historiographical community limited their phase space, compromising their capacity to explore emergent phenomena. The intricacy of the underlying conceptual model suited the ABM better than the board game, which struggled to reconcile complexity with good game design practices. The board game, however, proved to be an effective validating tool for the ABM. Limitations and Suggestions for Further Research: The project espoused an overt simulation- and game-centric approach, paying little attention to unguided play. Recent literature suggests that fostering, rather than hindering, playful exploration could address some of the pitfalls identified by this project. Conclusion: Play could be a means of reconciliation between simulational, ludic, and historiographical practices. However, to ensure that projects adhere to epistemic standards, it is recommended that a methodology is developed to integrate it into research in ways that can be tested and evaluated. [ABSTRACT FROM AUTHOR]

Residential areas in developing arid and semi-arid countries are highly vulnerable to flooding, and water shortages have forced their inhabitants to live close to rivers. While climate change is expected to cause more extreme weather conditions in the future, adopting private loss-reduction measures can diminish flood risk. Although the number of flood models has grown significantly for developing arid and semi-arid lands, these models suffer from being incapable of performing micro-scale flood risk analysis and including household behaviors. This research work presents a novel socio-economic simulation model in the framework of flood risk management (FRM) to couple household adaptive responses with flood risk analysis. Agent-based modeling (ABM) embeds human behaviors in a flood-simulating environment. The focus of this research is the upper Kan catchment in Iran with a long history of severe flash flooding. Our results show the ability of the developed framework to address household participation in FRM activities through private precautionary measures. Moreover, the results indicate the importance of presenting such micro-level behaviors in flood modeling for a more realistic flood risk assessment. It is also demonstrated that household adaptation in a continuous way can lead to less flood risks in the region, even under climate change and the future economy. Finally, the results reveal that the remaining and diminished regional flood risks are influenced by the behavioral framework through which the individuals make decisions in adopting flood-loss-reduction measures. A similar pattern is, however, observed in household contributions over time among the discussed behavioral approaches. [ABSTRACT FROM AUTHOR]

Guaranteeing efficient evacuations in urban communities is critical for preserving lives, minimizing disaster impacts, and promoting community resilience. Challenges such as high population density, limited evacuation routes, and communication breakdowns complicate evacuation efforts. Vulnerable populations, urban infrastructure constraints, and the increasing frequency of disasters further contribute to the complexity. Despite these challenges, the importance of timely evacuations lies in safeguarding human safety, enabling rapid disaster response, preserving critical infrastructure, and reducing economic losses. Overcoming these hurdles necessitates comprehensive planning, investment in resilient infrastructure, effective communication strategies, and continuous community engagement to foster preparedness and enhance evacuation efficiency. This research looks into the complexities of evacuation dynamics within urban residential areas, placing a particular focus on the interaction between joint-rental arrangements and family ties and their influence on evacuation strategies during emergency situations. Using agent-based modeling, evacuation simulation scenarios are implemented using the Changhongfang community (Shanghai) while systematically exploring how diverse interpersonal relationships impact the efficiency of evacuation processes. The adopted methodology encompasses a series of group experiments designed to determine the optimal proportions of joint-rental occupants within the community. Furthermore, the research examines the impact of various exit selection strategies on evacuation efficiency. Simulation outcomes shed light on the fundamental role of interpersonal factors in shaping the outcomes of emergency evacuations. Additionally, this study emphasizes the critical importance of strategic exit selections, revealing their potential to significantly enhance overall evacuation efficiency in urban settings. [ABSTRACT FROM AUTHOR]

• A framework is presented to explore the influence of friendships in a social network on drivers' route choice. • This paper simulates the evolution of drivers' route choice in a hypothetical transportation network under different assumptions. • This research offers insight into the route choice process when drivers can access information about other drivers' travel experience in a social network. • The simulation results show that the information types mainly affect the change rate, and the memory length mainly affects the time for convergence. Route choice is a decision that drivers face daily under varying traffic conditions. Although many studies have focused on route choice, the actual behavior considering real-world networks and the mechanisms that govern it have eluded complete characterization. Part of the difficulty has been in accounting for the growing availability of information sources that may influence drivers' route choices, precluding collection of adequate observational data. In addition, with the continuing emergence of new forms and sources of information, such as social media with varying degrees of interactivity, engagement, and immediacy, existing frameworks and models for representing these choices and the influence of new information sources have been lacking. This study uses an agent-based modeling approach to investigate the effects of social influence on drivers' route choices. In the model, all drivers communicate with each other in the same social network, and this information is combined with their previous experiences. The first aim of this study is to investigate the impact of social network connectivity and information exchange on route choice. The second is to characterize situations in which all drivers in the social network are satisfied with their route choices. The simulation results show that an equilibrium is achieved when all drivers in the given social network are satisfied with their current route choices. [ABSTRACT FROM AUTHOR]

AbstractIn probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation.To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model.Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions.The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs. [ABSTRACT FROM AUTHOR]

Theoretical agent-based models of residential and school choice have shown that substantial segregation can emerge as an (unintended) consequence of interactions between individual households and feedback mechanisms, despite households being relatively tolerant. However, for school choice, existing models have mostly been highly stylized, leaving open whether they are relevant for understanding school segregation in concrete empirical settings. To bridge this gap, this study develops an empirically calibrated agent-based model focusing on primary school choice in Amsterdam. Consistent with existing models, results show that substantial school segregation emerges when schools are chosen based on a trade-off between composition and distance, and also when households are relatively tolerant. Additionally, findings of (hypothetical) policy simulations suggest that it is important to understand which preferences for school composition and distance households have and how these interact. We find that the effects of policies aiming to reduce school segregation through geographical restricting mechanisms are highly dependent on those interacting preferences. Also, we assessed the contribution of residential segregation to school segregation. Our findings may have implications for methodologies aiming to estimate school choice preferences, such as discrete choice models, as these methodologies do not explicitly control for implications of these interactions and feedback mechanisms, which might lead to incorrect inference. [ABSTRACT FROM AUTHOR]

Standard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo. We describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach. Using two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption. MDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it. [ABSTRACT FROM AUTHOR]

The literature on organizational resilience explores various viewpoints, ranging from strategies to recover after disruptions to proactive anticipation of threats. Formal models primarily focus on the ability to recover from shocks, analyzing factors like deviation from performance targets, recovery time, and potential adaptation in function and structure. However, incorporating anticipation into such models remains scarce. Additionally, existing anticipatory systems models often neglect key aspects of organizational behavior. This work addresses these gaps by introducing an agent-based modeling approach that integrates anticipation into organizational decision-making. Our computational model features agents embedded in different organizational structures who make decisions based on projected market states (levels and trends). These decisions are subject to delays in perceiving market conditions and vary depending on the organization's adaptive capacity to update its offering. We analyze different organizational structures and market behaviors (trend direction and volatility). Our results indicate that full connectivity among agents can be detrimental to organizational resilience, as it may reduce the diversity of anticipation strategies for forecasting the market. Conversely, either sparse or highly clustered networks demonstrate a greater ability, on average, to keep up with changing market levels and trends. [ABSTRACT FROM AUTHOR]

The dynamics of pedestrian behavior within the built environment represent a multifaceted and evolving field of study, profoundly influenced by shifts in industrial and commercial paradigms. This systematic literature review (SLR) is motivated by the imperative to comprehensively investigate and assess the built environment through the lens of pedestrian modeling, employing advanced modeling tools. While previous scholarship has explored the interplay between the built environment and pedestrian dynamics (PD), there remains a conspicuous gap in research addressing the utilization of agent-based modeling (ABM) tools for a nuanced evaluation of PD within these contexts. The SLR highlights the essential and practical benefits of using ABM to study PD in built environments and combine related theories and practical projects. Beyond theoretical discussions, it emphasizes the real-world contributions of ABM in understanding and visualizing how people behave in urban spaces. It aims to provide deep insights for both researchers and urban planners. By thoroughly examining recent research, it not only explores the practical uses of ABM but also reveals its broad implications for various aspects of pedestrian behavior in built environments. As a result, this SLR becomes a key resource for understanding the crucial role of ABM in studying the complexities of our surroundings. The findings and discussion here highlight ABM's vital role in bridging the gap between theory and practice, improving our understanding of pedestrian behavior in urban settings. Furthermore, this study outlines promising avenues for future research, thereby fostering continued exploration and innovation in the dynamic realm of pedestrian behavior within built environments. [ABSTRACT FROM AUTHOR]

Incentive contracts often do not govern all task elements for which an employee is responsible. Prior research, particularly in the tradition of principal-agent theory, has studied incomplete incentive contracts as multi-task problems focusing on how to motivate the employee to incur effort for a not-contracted task element. Thus, emphasis is on the "vertical" relation between superior and subordinate, where both are modeled as gifted economic actors. This paper takes another perspective focusing on the "horizontal" interferences of—contracted and not-contracted—task elements across various employees in an organization and, hence, on the complexity of an organization's task environment. In order to disentangle the interactions among tasks from agents' behavior, the paper pursues a minimal intelligence approach. An agent-based simulation model based on the framework of NK fitness landscapes is employed. In the simulation experiments, artificial organizations search for superior performance, and the experiments control for the complexity of the task environment and the level of contractual incompleteness. The results suggest that the complexity of the task environment in terms of interactions among task elements may considerably shape the effects of incomplete incentive contracts. In particular, the results indicate that moderate incompleteness of incentive contracts may be beneficial with respect to organizational performance when intra-organizational complexity is high. This is caused by stabilization of search resulting from incomplete contracts. Moreover, interactions may induce that the not-contracted task elements could serve as means objectives, i.e., contributing to achieving contracted task elements. [ABSTRACT FROM AUTHOR]

A mob is an event that is organized via social media, email, SMS, or other forms of digital communication technologies in which a group of people (who might have an agenda) get together online or offline to collectively conduct an act and then disperse (quickly or over a long period). In recent years, these events are increasingly happening worldwide due to the anonymity of the internet, affordability of social media, boredom, etc. Studying such a phenomenon is difficult due to a lack of data, theoretical underpinning, and resources. In this research, we use the Agent-Based Modeling (ABM) technique to model the mobbers and the Monte Carlo method to assign random values to the factors extracted from the theory of Collective Action and conduct many simulations. We also leverage our previous research on Deviant Cyber Flash Mobs to implement various scenarios the mobber could face when they decide to act in a mob or not. This resulted in a model that can simulate mobs, estimate the mob success rate, and the needed powerful actors (e.g., mob organizers) for a mob to succeed. We finally evaluate our model using real-world mob data collected from the Meetup social media platform. This research is one step toward fully understanding mob formation and the motivations of its participants and organizers. [ABSTRACT FROM AUTHOR]

In actual pandemic situations like COVID-19, it is important to understand the influence of single mitigation measures as well as combinations to create most dynamic impact for lockdown scenarios. Therefore we created an agent-based model (ABM) to simulate the spread of SARS-CoV-2 in an abstract city model with several types of places and agents. In comparison to infection numbers in Germany our ABM could be shown to behave similarly during the first wave. In our model, we implemented the possibility to test the effectiveness of mitigation measures and lockdown scenarios on the course of the pandemic. In this context, we focused on parameters of local events as possible mitigation measures and ran simulations, including varying size, duration, frequency and the proportion of events. The majority of changes to single event parameters, with the exception of frequency, showed only a small influence on the overall course of the pandemic. By applying different lockdown scenarios in our simulations, we could observe drastic changes in the number of infections per day. Depending on the lockdown strategy, we even observed a delayed peak in infection numbers of the second wave. As an advantage of the developed ABM, it is possible to analyze the individual risk of single agents during the pandemic. In contrast to standard or adjusted ODEs, we observed a 21% (with masks) / 48% (without masks) increased risk for single reappearing participants on local events, with a linearly increasing risk based on the length of the events. [ABSTRACT FROM AUTHOR]

Introduction: Antibody–drug conjugates (ADCs) show significant clinical efficacy in the treatment of solid tumors, but a major limitation to their success is poor intratumoral distribution. Adding a carrier dose improves both distribution and overall drug efficacy of ADCs, but the optimal carrier dose has not been outlined for different payload classes. Objective: In this work, we study two carrier dose regimens: 1) matching payload potency to cellular delivery but potentially not reaching cells farther away from blood vessels, or 2) dosing to tumor saturation but risking a reduction in cell killing from a lower amount of payload delivered per cell. Methods: We use a validated computational model to test four different payloads conjugated to trastuzumab to determine the optimal carrier dose as a function of target expression, ADC dose, and payload potency. Results: We find that dosing to tumor saturation is more efficacious than matching payload potency to cellular delivery for all payloads because the increase in the number of cells targeted by the ADC outweighs the loss in cell killing on targeted cells. An important exception exists if the carrier dose reduces the payload uptake per cell to the point where all cell killing is lost. Likewise, receptor downregulation can mitigate the benefits of a carrier dose. Conclusions: Because tumor saturation and in vitro potency can be measured early in ADC design, these results provide insight into maximizing ADC efficacy and demonstrate the benefits of using simulation to guide ADC design. [ABSTRACT FROM AUTHOR]

Computational simulation models have been widely used to study the dynamics of COVID-19. Among those, bottom-up approaches such as agent-based models (ABMs) can account for population heterogeneity. While many studies have addressed COVID-19 spread at various scales, insufficient studies have investigated the spread of COVID-19 within closed indoor settings. This study aims to develop an ABM to simulate the spread of COVID-19 in a closed indoor setting using three transmission sub-models. Moreover, a comprehensive sensitivity analysis encompassing 4374 scenarios is performed. The model is calibrated using data from Calabria, Italy. The results indicated a decent consistency between the observed and predicted number of infected people (MAPE = 27.94%, RMSE = 0.87 and χ 2 (1 , N = 34) = (44.11 , p = 0.11) ). Notably, the transmission distance was identified as the most influential parameter in this model. In nearly all scenarios, this parameter had a significant impact on the outbreak dynamics (total cases and epidemic peak). Also, the calibration process showed that the movement of agents and the number of initial asymptomatic agents are vital model parameters to simulate COVID-19 spread accurately. The developed model may provide useful insights to investigate different scenarios and dynamics of other similar infectious diseases in closed indoor settings. [ABSTRACT FROM AUTHOR]

Optimizing Resource Allocation in Fire Departments (RAFD) is crucial for enhancing Fire Protection Services (FPS) and ultimately saving lives. Efficient RAFD ensures that fire departments have the necessary resources to respond effectively to emergencies. This paper presents a method for optimizing RAFD based on performance assessment results, examining its impact on Fire Department (FD) efficiency in Portugal. Evaluating data from 353 FDs, two RAFD optimization methods were assessed: one adhering to Portuguese regulations and constraints, such as budget allocation limitations, and another without such constraints. Integrating a slack-based data envelopment analysis model and mixed-integer linear programming, the study found that incorporating FD efficiency scores in RAFD improved overall efficiency at national, district, and FD levels. While adherence to Portuguese regulations led to balanced resource allocation and a 4% performance improvement at the national level, relaxing constraints yielded an 8% improvement, albeit with potential performance deterioration in some FDs. The detailed budget and efficiency metric analysis provided in this paper offers actionable insights for fire protection services enhancement. This underscores the importance of diverse optimization strategies to enhance FD efficiency, with implications for decision-makers at the Portuguese National Authority for Emergency and Civil Protection and similar organizations globally. [ABSTRACT FROM AUTHOR]