Hybrid attacks coordinate the exploitation of vulnerabilities across domains to undermine trust in authorities and cause social unrest. Whilst such attacks have primarily been seen in active conflict zones, there is growing concern about the potential harm that can be caused by hybrid attacks more generally and a desire to discover how better to identify and react to them. In addressing such threats, it is important to be able to identify and understand an adversary’s behaviour. Game theory is the approach predominantly used in security and defence literature for this purpose. However, the underlying rationality assumption, the equilibrium concept of game theory, as well as the need to make simplifying assumptions can limit its use in the study of emerging threats. To study hybrid threats, we present a novel agent-based model in which, for the first time, agents use reinforcement learning to inform their decisions. This model allows us to investigate the behavioural strategies of threat agents with hybrid attack capabilities as well as their broader impact on the behaviours and opinions of other agents.

PopComp by Andre Costopoulos 2020
andre.costopoulos@ualberta.ca
Licence: DWYWWI (Do whatever you want with it)

I use Netlogo to build a simple environmental change and population expansion and diffusion model. Patches have a carrying capacity and can host two kinds of populations (APop and BPop). Each time step, the carrying capacity of each patch has a given probability of increasing or decreasing up to a maximum proportion.

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This is the final version of the model. To simulate the normative dynamics we used the EmIL (EMergence In the Loop) Framework which was kindly provided by Ulf Lotzmann. http://cfpm.org/EMIL-D5.1.pdf

While the world’s total urban population continues to grow, this growth is not equal. Some cities are declining, resulting in urban shrinkage which is now a global phenomenon. Many problems emerge due to urban shrinkage including population loss, economic depression, vacant properties and the contraction of housing markets. To explore this issue, this paper presents an agent-based model stylized on spatially explicit data of Detroit Tri-county area, an area witnessing urban shrinkage. Specifically, the model examines how micro-level housing trades impact urban shrinkage by capturing interactions between sellers and buyers within different sub-housing markets. The stylized model results highlight not only how we can simulate housing transactions but the aggregate market conditions relating to urban shrinkage (i.e., the contraction of housing markets). To this end, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to alleviate this issue.

Zooarchaeological evidences indicate that rabbit hunting became prevalent during the Upper Palaeolithic in the Iberian Peninsula.

The purpose of the ABM is to test if warren hunting using nets as a collective strategy can explain the introduction of rabbits in the human diet in the Iberian Peninsula during this period. It is analyzed whether this hunting strategy has an impact on human diet breadth by affecting the relative abundance of other main taxa in the dietary spectrum.
Model validity is measured by comparing simulated diet breadth to the observed diet breadth in the zooarchaeological record.

The agent-based model is explicitly grounded on the Diet Breadth Model (DBM), from the Optimal Foraging Theory (OFT).
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Discriminators who have limited tolerance for helping dissimilar others are necessary for the evolution of costly cooperation in a one-shot Prisoner’s Dilemma. Existing research reports that trust in

We present an agent-based model of worker protest informed by Epstein (2002). Workers have varying degrees of grievance depending on the difference between their wage and the average of their neighbors. They protest with probabilities proportional to grievance, but are inhibited by the risk of being arrested – which is determined by the ratio of coercive agents to probable rebels in the local area. We explore the effect of similarity perception on the dynamics of collective behavior. If […]

The agent-based simulation of land-use governance (ABSOLUG) is a NetLogo model designed to explore the interactions between stakeholders and the impact of multi-stakeholder governance approaches on tropical deforestation. The purpose of ABSOLUG is to advance our understanding of land use governance, identify macro-level patterns of interaction among governments, commodity producers, and NGOs in tropical deforestation frontiers, and to set a foundation for generating middle-range theories for multi-stakeholder governance approaches. The model represents a simplified, generic, tropical commodity production system, as opposed to a specific empirical case, and as such aims to generate interpretable macro-level patterns that are based on plausible, micro-level behavioral rules. It is designed for scientists interested in land use governance of tropical commodity production systems, and for decision- and policy-makers seeking to develop or enhance governance schemes in multi-stakeholder commodity systems.

The FishCensus model simulates underwater visual census methods, where a diver estimates the abundance of fish. A separate model is used to shape species behaviours and save them to a file that can be shared and used by the counting model.

This model implements a Bayesian belief revision model that contrasts an ideal agent in possesion of true likelihoods, an agent using a fixed estimate of trusting its source of information, and an agent updating its trust estimate.