The model simulates tail biting behaviour in pigs and how they can turn into a biter and/or victim. The effect of a redirected motivation, behavioural changes in victims and preference to bite a lying pig on tail biting can be tested in the model

This model includes an innovation search environment. Agents search and can share their findings. It’s implemented in Netlogo-Hubnet & a parallel Netlogo model. This allows for validation of search strategies against experimental findings.

This program simulates a group of hunter-gatherer (households) moving randomly over an artificial landscapoe pulated with resources randomly distributed (a Gaussian distribution). To survive, agents hunt and gather using their own labor resources and available technology. When labor and technology is not enough to compensate the resource difficulty of access, they need to cooperate. The purpose of the model is to analyze the consequences of cooperation on cultural diversity: the more the agents cooperate, the more their culture (a 10 componenet vector) is updated to imitate the culture of cooperative agents. The less the agent cooperates, the more different its culture becomes.

SWIM is a simulation of water management, designed to study interactions among water managers and customers in Phoenix and Tucson, Arizona. The simulation can be used to study manager interaction in Phoenix, manager and customer messaging and water conservation in Tucson, and when coupled to the Water Balance Model (U New Hampshire), impacts of management and consumer choices on regional hydrology.

Publications:

Murphy, John T., Jonathan Ozik, Nicholson T. Collier, Mark Altaweel, Richard B. Lammers, Alexander A. Prusevich, Andrew Kliskey, and Lilian Alessa. “Simulating Regional Hydrology and Water Management: An Integrated Agent-Based Approach.” Winter Simulation Conference, Huntington Beach, CA, 2015.

Schelling and Sakoda prominently proposed computational models suggesting that strong ethnic residential segregation can be the unintended outcome of a self-reinforcing dynamic driven by choices of individuals with rather tolerant ethnic preferences. There are only few attempts to apply this view to school choice, another important arena in which ethnic segregation occurs. In the current paper, we explore with an agent-based theoretical model similar to those proposed for residential segregation, how ethnic tolerance among parents can affect the level of school segregation. More specifically, we ask whether and under which conditions school segregation could be reduced if more parents hold tolerant ethnic preferences. We move beyond earlier models of school segregation in three ways. First, we model individual school choices using a random utility discrete choice approach. Second, we vary the pattern of ethnic segregation in the residential context of school choices systematically, comparing residential maps in which segregation is unrelated to parents’ level of tolerance to residential maps reflecting their ethnic preferences. Third, we introduce heterogeneity in tolerance levels among parents belonging to the same group. Our simulation experiments suggest that ethnic school segregation can be a very robust phenomenon, occurring even when about half of the population prefers mixed to segregated schools. However, we also identify a “sweet spot” in the parameter space in which a larger proportion of tolerant parents makes the biggest difference. This is the case when parents have moderate preferences for nearby schools and there is only little residential segregation. Further experiments are presented that unravel the underlying mechanisms.

Negotiation Lab 1.0 is an agent-based model of peace negotiations that explores how the parties’ readiness — their motivation and optimism to engage in talks — evolves dynamically throughout the negotiation process. The model reconceptualizes readiness as an adaptive state variable that is continuously updated through feedback from negotiation outcomes, rather than a static precondition assessed at the onset of talks.
The model simulates two parties negotiating a multi-issue agenda. In each round, parties allocate effort to the current sub-issue; outcomes depend on their joint effort and a stochastic component representing external factors. Results feed back into each party’s readiness, shaping subsequent engagement. The negotiation ends either when all agenda items are resolved (agreement) or when a party’s readiness falls below a critical threshold (breakdown).
Key parameters include the initial readiness of each party, agenda structure (balanced, hard, easy, red, or random), type of negotiation (from highly cooperative to highly competitive), and each party’s effort strategy (always high, always low, random, or pseudo tit-for-tat). The model shows that while initial readiness is associated with negotiation outcomes, it is neither necessary nor sufficient to determine them: process variables — the type of interaction, agenda design, and adaptive effort strategies — exert comparatively larger effects on outcomes. Identical initial conditions can produce widely divergent trajectories, illustrating path dependence and sensitivity to feedback dynamics.
The model is implemented in NetLogo 7.0 and is documented using the ODD+D protocol. It is associated with the paper “Beyond Initial Conditions: How Adaptive Readiness Shapes Peace Negotiation Outcomes” (Arévalo, under review).

A computational model of a classic small group study by Alex Bavelas. This computational model was designed to explore the difficulty in translating a seemingly simple real-world experiment into a computational model.

The model analyzes the economic and ecological effects of a provision of livestock drought insurance for dryland pastoralists. More precisely, it yields qualitative insights into how long-term herd and pasture dynamics change through insurance.

Crowd dynamics have important applications in evacuation management systems relevant to organizing safer large scale gatherings. For crowd safety, it is very important to study the evolution of potential crowd behaviours by simulating the crowd evacuation process. Planning crowd control tasks by studying the impact of crowd behaviour evolution towards evacuation could mitigate the possibility of crowd disasters. During a typical emergency evacuation scenario, conflict among agents occurs when agents intend to move to the same location as a result of the interaction with their nearest neighbours. The effect of the agent response towards their neighbourhood is vital in order to understand the effect of variation of crowd behaviour on the whole environment. In this work, we model crowd motion subject to exit congestion under uncertainty conditions in a continuous space via computer simulations. We model best-response, risk-seeking, risk-averse and risk-neutral behaviours of agents via certain game theoretic notions. We perform computer simulations with heterogeneous populations in order to study the effect of the evolution of agent behaviours towards egress flow under threat conditions. Our simulation results show the relation between the local crowd pressure and the number of injured agents. We observe that when the proportion of agents in a population of risk-seeking agents is increased, the average crowd pressure, average local density and the number of injured agents increases. Besides that, based on our simulation results, we can infer that crowd disasters could be prevented if the agent population consists entirely of risk-averse and risk-neutral agents despite circumstances that lead to threats.

This model explores different aspects of the formation of urban neighbourhoods where residents believe in values distant from those dominant in society. Or, at least, this is what the Danish government beliefs when they discuss their politics about parallel societies. This simulation is set to understand (a) whether these alternative values areas form and what determines their formation, (b) if they are linked to low or no income residents, and (c) what happens if they disappear from the map. All these three points are part of the Danish government policy. This agent-based model is set to understand the boundaries and effects of this policy.