IOP 2.1.2 is an agent-based simulation model designed to explore the relations between (1) employees, (2) tasks and (3) resources in an organizational setting. By comparing alternative cognitive strategies in the use of resources, employees face increasingly demanding waves of tasks that derive by challenges the organization face to adapt to a turbulent environment. The assumption tested by this model is that a successful organizational adaptation, called plastic, is necessarily tied to how employees handle pressure coming from existing and new tasks. By comparing alternative cognitive strategies, connected to ‘docility’ (Simon, 1993; Secchi, 2011) and ‘extended’ cognition (Clark, 2003, Secchi & Cowley, 2018), IOP 2.1.2 is an attempt to indicate which strategy is most suitable and under which scenario.

An artifcal stock market model that allows users to vary the number of risky assets as well as the network topology that investors forms in an attempt to understand the dynamics of the market.

The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure

Schelling famously proposed an extremely simple but highly illustrative social mechanism to understand how strong ethnic segregation could arise in a world where individuals do not necessarily want it. Schelling’s simple computational model is the starting point for our extensions in which we build upon Wilensky’s original NetLogo implementation of this model. Our two NetLogo models can be best studied while reading our chapter “Agent-based Computational Models” (Flache and de Matos Fernandes, 2021). In the chapter, we propose 10 best practices to elucidate how agent-based models are a unique method for providing and analyzing formally precise, and empirically plausible mechanistic explanations of puzzling social phenomena, such as segregation, in the social world. Our chapter addresses in particular analytical sociologists who are new to ABMs.

In the first model (SegregationExtended), we build on Wilensky’s implementation of Schelling’s model which is available in NetLogo library (Wilensky, 1997). We considerably extend this model, allowing in particular to include larger neighborhoods and a population with four groups roughly resembling the ethnic composition of a contemporary large U.S. city. Further features added concern the possibility to include random noise, and the addition of a number of new outcome measures tuned to highlight macro-level implications of the segregation dynamics for different groups in the agent society.

In SegregationDiscreteChoice, we further modify the model incorporating in particular three new features: 1) heterogeneous preferences roughly based on empirical research categorizing agents into low, medium, and highly tolerant within each of the ethnic subgroups of the population, 2) we drop global thresholds (%-similar-wanted) and introduce instead a continuous individual-level single-peaked preference function for agents’ ideal neighborhood composition, and 3) we use a discrete choice model according to which agents probabilistically decide whether to move to a vacant spot or stay in the current spot by comparing the attractiveness of both locations based on the individual preference functions.

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Identifying how organisms respond to environmental stressors remains of central importance as human impacts continue to shift the environmental conditions for countless species. Some mammals are able to mitigate these environmental stressors at the cellular level, but the mechanisms by which cells are able to do this and how these strategies vary among species is not well understood. At the cellular level, it is difficult to identify the temporal dynamics of the system through empirical data because fine-grained time course samples are both incomplete and limited by available resources. To help identify the mechanisms by which animal cells mitigate extreme environmental conditions, we propose an agent-based model to capture the dynamics of the system. In the model, agents are regulatory elements and genes, and are able to impact the behaviors of each other. Rather than imposing rules for these interactions among agents, we will begin with randomized sets of rules and calibrate the model based on empirical data of cellular responses to stress. We will apply a common-garden framework to cultured cells from 16 mammalian species, which will yield genomic data and measures of cell morphology and physiology when exposed to different levels of temperature, glucose, and oxygen. These species include humans, dolphins, bats, and camels, among others, which vary in how they respond to environmental stressors, offering a comparative approach for identifying mechanistic rules whereby cells achieve robustness to environmental stressors. For calibration of the model, we will iteratively select for rules that best lead to the emergent outcomes observed in the cellular assays. Our model is generalized for any species, any cell type, and any environmental stressor, offering many applications of the model beyond our study. This study will increase our understanding of how organisms mitigate environmental stressors at the cellular level such that we can better address how organisms are impacted by and respond to extreme environmental conditions.

A model of innovation diffusion in a structured population with two groups who are averse to adopting a produce popular with the outgroup.

This is an interdisciplinary agent-based model with Monte Carlo simulations to assess the relative effects of broadcast and contagion processes in a multiplex social network. This multiplex approach models multiple channels of informal communication - phone, word-of-mouth, and social media - that vary in their attribute values. Each agent is an individual in a threatened community who, once warned, has a probability of warning others in their social network using one of these channels. The probability of an individual warning others is based on their warning source and the time remaining until disaster impact, among other variables. Default parameter values were chosen from empirical studies of disaster warnings along with the spatial aspects of Coos Bay, OR, USA and Seaside, OR, USA communities.

This is a model of coherency based belief within a dynamic network of individuals. Described in an invited talk on workshop on Coherence, Berlin, 9th July 2016.

This generic agent-based model simulates the evolution of agent’s opinions through their exchange of arguments.
The idea behind this model is to explicitly represent the process of mental deliberation of agents from arguments to an opinion, through the use of Dung’s argumentation framework complemented by a structured description of arguments. An application of the model on the diffusion of vegetarian diets is proposed.

The purpose of the simulation is to evaluate alternative interventions by a value chain development program, aiming to improve rural livelihood and food and nutrition security. In northern Ghana, where distrust between the partners can be a problem in the functioning of value chains, the program supports the incorporation of smallholder farmers in soy clusters or agriculture APEX organization (farmers’ co-operatives) with a fair business environment. The goal is to to include the smallholder farmers in a strong value chain and reduce distrust.