This BNE-informed ABM ultimately aims to provide a more realistic description of complicated pedestrian behaviours especially in high-density and life-threatening situations. Bayesian Nash Equilibrium (BNE) was adopted to reproduce interactive decision-making process among rational and game-playing agents. The implementations of 3 behavioural models, which are Shortest Route (SR) model, Random Follow (RF) model, and BNE model, make it possible to simulate emergent patterns of pedestrian behaviours (e.g. herding and self-organised queuing behaviours, etc.) in emergency situations.

According to the common features of previous mass trampling accidents, a series of simulation experiments were performed in space with 3 types of barriers, which are Horizontal Corridors, Vertical Corridors, and Random Squares, standing for corridors, bottlenecks and intersections respectively, to investigate emergent behaviours of evacuees in varied constricted spatial environments. The output of this ABM has been available at https://data.mendeley.com/datasets/9v4byyvgxh/1.

The Agent-Based Ramsey growth model is designed to analyze and test a decentralized economy composed of utility maximizing agents, with a particular focus on understanding the growth dynamics of the system. We consider farms that adopt different investment strategies based on the information available to them. The model is built upon the well-known Ramsey growth model, with the introduction of endogenous technical progress through mechanisms of learning by doing and knowledge spillovers.

FIsheries Simulation with Human COmplex DEcision-making (FISHCODE) is an agent-based model to depict and analyze current and future spatio-temporal dynamics of three German fishing fleets in the southern North Sea. Every agent (fishing vessel) makes daily decisions about if, what, and how long to fish. Weather, fuel and fish prices, as well as the actions of their colleagues influence agents’ decisions. To combine behavioral theories and enable agents to make dynamic decision, we implemented the Consumat approach, a framework in which agents’ decisions vary in complexity and social engagement depending on their satisfaction and uncertainty. Every agent has three satisfactions and two uncertainties representing different behavioral aspects, i.e. habitual behavior, profit maximization, competition, conformism, and planning insecurity. Availability of extensive information on fishing trips allowed us to parameterize many model parameters directly from data, while others were calibrated using pattern oriented modelling. Model validation showed that spatial and temporal aggregated ABM outputs were in realistic ranges when compared to observed data. Our ABM hence represents a tool to assess the impact of the ever growing challenges to North Sea fisheries and provides insight into fisher behavior beyond profit maximization.

Criminal organizations operate in complex changing environments. Being flexible and dynamic allows criminal networks not only to exploit new illicit opportunities but also to react to law enforcement attempts at disruption, enhancing the persistence of these networks over time. Most studies investigating network disruption have examined organizational structures before and after the arrests of some actors but have disregarded groups’ adaptation strategies.
MADTOR simulates drug trafficking and dealing activities by organized criminal groups and their reactions to law enforcement attempts at disruption. The simulation relied on information retrieved from a detailed court order against a large-scale Italian drug trafficking organization (DTO) and from the literature.
The results showed that the higher the proportion of members arrested, the greater the challenges for DTOs, with higher rates of disrupted organizations and long-term consequences for surviving DTOs. Second, targeting members performing specific tasks had different impacts on DTO resilience: targeting traffickers resulted in the highest rates of DTO disruption, while targeting actors in charge of more redundant tasks (e.g., retailers) had smaller but significant impacts. Third, the model examined the resistance and resilience of DTOs adopting different strategies in the security/efficiency trade-off. Efficient DTOs were more resilient, outperforming secure DTOs in terms of reactions to a single, equal attempt at disruption. Conversely, secure DTOs were more resistant, displaying higher survival rates than efficient DTOs when considering the differentiated frequency and effectiveness of law enforcement interventions on DTOs having different focuses in the security/efficiency trade-off.
Overall, the model demonstrated that law enforcement interventions are often critical events for DTOs, with high rates of both first intention (i.e., DTOs directly disrupted by the intervention) and second intention (i.e., DTOs terminating their activities due to the unsustainability of the intervention’s short-term consequences) culminating in dismantlement. However, surviving DTOs always displayed a high level of resilience, with effective strategies in place to react to threatening events and to continue drug trafficking and dealing.

This model presents the simulation model of a city in the context of overtourism. The study area is the city of Santa Marta in Colombia. The purpose is to illustrate the spatial and temporal distribution of population and tourists in the city. The simulation analyzes emerging patterns that result from the interaction between critical components in the touristic urban system: residents, urban space, touristic sites, and tourists. The model is an Agent-Based Model (ABM) with the GAMA software. Also, it used public input data from statistical centers, geographical information systems, tourist websites, reports, and academic articles. The ABM includes assessing some measures used to address overtourism. This is a field of research with a low level of analysis for destinations with overtourism, but the ABM model allows it. The results indicate that the city has a high risk of overtourism, with spatial and temporal differences in the population distribution, and it illustrates the effects of two management measures of the phenomenon on different scales. Another interesting result is the proposed tourism intensity indicator (OVsm), taking into account that the tourism intensity indicators used by the literature on overtourism have an overestimation of tourism pressures.

A road freight transport (RFT) operation involves the participation of several types of companies in its execution. The TRANSOPE model simulates the subcontracting process between 3 types of companies: Freight Forwarders (FF), Transport Companies (TC) and self-employed carriers (CA). These companies (agents) form transport outsourcing chains (TOCs) by making decisions based on supplier selection criteria and transaction acceptance criteria. Through their participation in TOCs, companies are able to learn and exchange information, so that knowledge becomes another important factor in new collaborations. The model can replicate multiple subcontracting situations at a local and regional geographic level.
The succession of n operations over d days provides two types of results: 1) Social Complex Networks, and 2) Spatial knowledge accumulation environments. The combination of these results is used to identify the emergence of new logistics clusters. The types of actors involved as well as the variables and parameters used have their justification in a survey of transport experts and in the existing literature on the subject.
As a result of a preferential selection process, the distribution of activity among agents shows to be highly uneven. The cumulative network resulting from the self-organisation of the system suggests a structure similar to scale-free networks (Albert & Barabási, 2001). In this sense, new agents join the network according to the needs of the market. Similarly, the network of preferential relationships persists over time. Here, knowledge transfer plays a key role in the assignment of central connector roles, whose participation in the outsourcing network is even more decisive in situations of scarcity of transport contracts.

This model is intended to study the way information is collectively managed (i.e. shared, collected, processed, and stored) in a system and how it performs during a crisis or disaster. Performance is assessed in terms of the system’s ability to provide the information needed to the actors who need it when they need it. There are two main types of actors in the simulation, namely communities and professional responders. Their ability to exchange information is crucial to improve the system’s performance as each of them has direct access to only part of the information they need.

In a nutshell, the following occurs during a simulation. Due to a disaster, a series of randomly occurring disruptive events takes place. The actors in the simulation need to keep track of such events. Specifically, each event generates information needs for the different actors, which increases the information gaps (i.e. the “piles” of unaddressed information needs). In order to reduce the information gaps, the actors need to “discover” the pieces of information they need. The desired behavior or performance of the system is to keep the information gaps as low as possible, which is to address as many information needs as possible as they occur.

This project combines game theory and genetic algorithms in a simulation model for evolutionary learning and strategic behavior. It is often observed in the real world that strategic scenarios change over time, and deciding agents need to adapt to new information and environmental structures. Yet, game theory models often focus on static games, even for dynamic and temporal analyses. This simulation model introduces a heuristic procedure that enables these changes in strategic scenarios with Genetic Algorithms. Using normalized 2x2 strategic-form games as input, computational agents can interact and make decisions using three pre-defined decision rules: Nash Equilibrium, Hurwicz Rule, and Random. The games then are allowed to change over time as a function of the agent’s behavior through crossover and mutation. As a result, strategic behavior can be modeled in several simulated scenarios, and their impacts and outcomes can be analyzed, potentially transforming conflictual situations into harmony.

The purpose of the study is to unpack and explore a potentially beneficial role of sharing metacognitive information within a group when making repeated decisions about common pool resource (CPR) use.

We explore the explanatory power of sharing metacognition by varying (a) the individual errors in judgement (myside-bias); (b) the ways of reaching a collective judgement (metacognition-dependent), (c) individual knowledge updating (metacognition- dependent) and d) the decision making context.

The model (AgentEx-Meta) represents an extension to an existing and validated model reflecting behavioural CPR laboratory experiments (Schill, Lindahl & Crépin, 2015; Lindahl, Crépin & Schill, 2016). AgentEx-Meta allows us to systematically vary the extent to which metacognitive information is available to agents, and to explore the boundary conditions of group benefits of metacognitive information.

The Communicating Hazard Information in the Modern Environment (CHIME) agent-based model (ABM) is a Netlogo program that facilitates the analysis of information flow and protective decisions across space and time during hazardous weather events. CHIME ABM provides a platform for testing hypotheses about collective human responses to weather forecasts and information flow, using empirical data from historical hurricanes. The model uses real world geographical and hurricane data to set the boundaries of the simulation, and it uses historical hurricane forecast information from the National Hurricane Center to initiate forecast information flow to citizen agents in the model.