The role of human hunting behavior versus climate change in the mass extinction of megafauna during the Late Quaternary is much debated. To move beyond monocausal arguments, we treat human–megafauna–environment relationships as social–ecological systems from a complex adaptive systems perspective, to create an agent-based model that tests how human hunting may interact with environmental stress and animal life history to affect the probability of extinction. Using the extinction of Syncerus antiquus in South Africa at 12–10 ka as a loose inspirational case study, we parameterized a set of experiments to identify cross-feedbacks among environmental dynamics, prey life history, and human hunting pressure that affect extinction probability in a non-linear way. An important anthropogenic boundary condition emerges when hunting strategies interrupt prey animal breeding cycles. This effect is amplified in patchy, highly seasonal environments to increase the chances of extinction. This modeling approach to human behavior and biodiversity loss helps us understand how these types of cross-feedback effects and boundary conditions emerge as system components interact and change. We argue that this approach can help translate archaeological data and insight about past extinction for use in understanding and combating the current mass extinction crisis.

The neutral model of stone procurement developed by Brantingham (2003, 2006) provides a formal means to investigate the formation of lithic discard patterning under changing forager mobility conditions. This study modifies Brantingham's (2006) Lévy walk model to examine the influence of discard probability on the spatial distribution of raw material abundance. The model outcome shows that forager movement and tool discard probability have similar effects on the simulated patterns of raw material transport, so it is difficult—if not impossible—to differentiate the respective influence of the two factors from distance to source distributions alone. This finding of equifinality complicates the task of interpretating hominin mobility from archaeological distance to source data, particularly in settings such as the Middle-Upper Paleolithic transition, which is marked by an important reorganization in hominin lithic technology that may have affected stone tool discard probability.

Agent-based social simulations have historically been evaluated using two criteria: verification and validation. This article questions the adequacy of this dual evaluation scheme. It claims that the scheme does not conform to everyday practices of evaluation, and has, over time, fostered a theory-practice gap in the assessment of social simulations. This gap originates because the dual evaluation scheme, inherited from computer science and software engineering, on one hand, overemphasizes the technical and formal aspects of the implementation process and, on the other hand, misrepresents the connection between the conceptual and the computational model. The mismatch between evaluation theory and practice, it is suggested, might be overcome if practitioners of agent-based social simulation adopt a single criterion evaluation scheme in which: i) the technical/formal issues of the implementation process are tackled as a matter of debugging or instrument calibration, and ii) the epistemological issues surrounding the connection between conceptual and computational models are addressed as a matter of validation.

Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.

Stem cells give rise to the entirety of cells within an organ. Maintaining stem cell identity and coordinately regulating stem cell divisions is crucial for proper development. In plants, mobile proteins, such as WUSCHEL-RELATED HOMEOBOX 5 (WOX5) and SHORTROOT (SHR), regulate divisions in the root stem cell niche. However, how these proteins coordinately function to establish systemic behaviour is not well understood. We propose a non-cell autonomous role for WOX5 in the cortex endodermis initial (CEI) and identify a regulator, ANGUSTIFOLIA (AN3)/GRF-INTERACTING FACTOR 1, that coordinates CEI divisions. Here, we show with a multi-scale hybrid model integrating ordinary differential equations (ODEs) and agent-based modeling that quiescent center (QC) and CEI divisions have different dynamics. Specifically, by combining continuous models to describe regulatory networks and agent-based rules, we model systemic behaviour, which led us to predict cell-type-specific expression dynamics of SHR, SCARECROW, WOX5, AN3 and CYCLIND6;1, and experimentally validate CEI cell divisions. Conclusively, our results show an interdependency between CEI and QC divisions.

Agricultural activities have imposed significant impacts on water resources, leading to hypoxic zones and harmful algal blooms all over the world. Government agencies, nongovernmental organizations, and individuals have been making various efforts to reduce this non-point source pollution. Among those efforts, even the more cost-effective examples of performance-based environmental payment programs generally have low participation rates. We investigate the effects of externalities in farmers’ decisions on neighboring farms, incorporating both a knowledge spillover effect and a positive environmental outcome externality of farmers’ best-management practice (BMP) adoption decisions. Our focus is on how these effects may influence the outcome of performance-based payment programs and how policy makers might recognize these effects in the design of cost-effective policies to promote program participation and BMP adoption. Rather than imposing an assumption of profit-maximization or forward-looking behavior, we allow outcomes to emerge from interactions among neighboring farmers. We recommend cost-effective policies across communities depending on their composition. It is more cost-effective to target communities with fewer innovators and/or target the programs towards the least-innovative individuals.

The performance of a design team is influenced by each team member’s unique cognitive style – i.e., their preferred manner of managing structure as they solve problems, make decisions, and seek to bring about change. Cognitive style plays an important role in how teams of engineers design and collaborate, but the interactions of cognitive style with team organization and processes have not been well studied. The limitations of small-scale behavioral experiments have led researchers to develop computational models for simulating teamwork; however, none have modeled the effects of individuals’ cognitive styles. This paper presents the Kirton Adaption–Innovation Inventory agent-based organizational optimization model (KABOOM), the first agent-based model of teamwork to incorporate cognitive style. In KABOOM, heterogeneous agents imitate the diverse problem-solving styles described by the Kirton Adaption-Innovation construct, which places each individual somewhere along the spectrum of cognitive style preference. Using the model, we investigate the interacting effects of a team’s communication patterns, specialization, and cognitive style composition on design performance. By simulating cognitive style in the context of team problem solving, KABOOM lays the groundwork for the development of team simulations that reflect humans’ diverse problem-solving styles.

A major challenge facing archaeologists is communicating our research to the public. Thankfully, new computational tools have enabled the testing and visualization of complex ideas in an easily packageable format. In this article we illustrate not only how agent-based modeling provides a platform for communicating complex ideas, but also how these game-like computer models can be explored and manipulated by members of the public therefore increasing their engagement in archaeological explanations. We suggest that these new digital tools serve as an excellent aid for education on the importance of archaeological sites and artifacts. To illustrate the above we walk the reader through a step-by-step pipeline of how to run an ABM model as an experiment and how to export it into a form ready to be sent to SHPO and THPO offices in tandem with reports. Ultimately, we hope that this work will help demystify the computational archaeology process and lead to more fluency in using agent-based modeling in research and outreach.

Archaeologists are using spatial data in increasingly sophisticated analyses and invoking more explicit considerations of space in their interpretations. Geographic information systems (GIS) have become standard technology for professional archaeologists in the collection and management of spatial data. Many calls have been made to develop and adapt digital geospatial technologies for interpretation and understanding past social dynamics, but this has been limited to some extent by the static nature of map-oriented GIS approaches. Here, we illustrate how coupling GIS with agent-based modeling (ABM) can assist with more dynamic explorations of past uses of space and geospatial phenomena.

Recent empirical research has documented the clustered volatility and fat tails of return distribution in stock markets, yet returns are uncorrelated over time. Certain agent-based theoretical models attempt to explain the empirical features in terms of investors' order-splitting or dynamic switching strategies, both of which are frequently used by actual stock investors. However, little theoretical research has discriminated among the behavioral assumptions within a model and compared the impacts of the assumptions on the empirical features. Nor has the research simultaneously replicated the return features and empirical features on market microstructure, such as patterns of order choice. This study constructs an artificial limit order market in which investors split orders into small pieces or use fundamental and trend-following predictors interchangeably over time. We demonstrate that, on one hand, the market that features strategies with order splitting and dynamic predictor selection can independently replicate clustered volatility and fat tails with near-zero return autocorrelations. However, we also show that patterns of order choice do not match those found in certain previous empirical studies in both types of economies. Thus, we conclude that, in reality, the two strategies can work to generate the empirical return features but that investors may also use other strategies in actual stock markets. We also demonstrate that the impact of both strategies on the volatility persistence tends to be greater as the number of traders increases in the market; this finding implies that the order-splitting strategy and dynamic predictor selection are more crucial for the empirical phenomena pertaining to larger capital stocks.

Dutch coastal land markets are characterized by high amenity values but are threatened by potential coastal hazards, leading to high potential damage costs from flooding. Yet, Dutch residents generally perceive low or no flood risk. Using an agent-based land market model and Dutch survey data on risk perceptions and location preferences, this paper explores the patterns of land development and land rents produced by buyers with low, highly skewed risk perceptions. We find that, compared to representative agent and uniform risk perception models, the skewed risk perception distribution produces substantially more, high-valued development in risky coastal zones, potentially creating economically significant risks triggered by the current Dutch flood protection policy.

The paper presents an agent-based model to study the possible effects of different fiscal and monetary policies in the context of debt deflation. We introduce a modified Taylor rule that includes the financial position of firms as a target. Monte Carlo simulations provide a representation of the complex feedback effects generated by the interaction among the different transmission channels of monetary policy. The model also reproduces the evidence of low inflation during stock market booms and shows how it can lead to overinvestment and destabilize the system. The paper also investigates the possible reasons behind this stylized fact by testing different behavioral rules for the central bank. We find that, in a context of sticky prices and volatile expectations, endogenous credit creation can be identified as the main source of the divergent dynamics of prices in the real and financial sectors.

About twenty five years ago the first discrete mathematical model of the immune systemwas proposed. It was very simple and stylized. Later, many other computational models havebeen proposed each one adding a certain level of sophistication and detail to thedescription of the system. One of these, the Celada-Seiden model published back in 1992,was already mature at its birth, setting apart from the topic-specific nature of the othermodels. This one was not just a model but rather a framework with which one couldimplement his own immunological theories.

Here we describe this computational framework, developed to perform simulations ofdifferent pathologies that are directly or indirectly connected to the immune system. Webriefly describe the system first, then we report on few applications so to give thereader a clear idea of its practical utility in clinical research problems.