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The model presented here is extensively described in the paper ‘Talk less to strangers: How homophily can improve collective decision-making in diverse teams’ (forthcoming at JASSS). A full replication package reproducing all results presented in the paper is accessible at https://osf.io/76hfm/.
Narrative documentation includes a detailed description of the model, including a schematic figure and an extensive representation of the model in pseudocode.
The model develops a formal representation of a diverse work team facing a decision problem as implemented in the experimental setup of the hidden-profile paradigm. We implement a setup where a group seeks to identify the best out of a set of possible decision options. Individuals are equipped with different pieces of information that need to be combined to identify the best option. To this end, we assume a team of N agents. Each agent belongs to one of M groups where each group consists of agents who share a common identity.
The virtual teams in our model face a decision problem, in that the best option out of a set of J discrete options needs to be identified. Every team member forms her own belief about which decision option is best but is open to influence by other team members. Influence is implemented as a sequence of communication events. Agents choose an interaction partner according to homophily h and take turns in sharing an argument with an interaction partner. Every time an argument is emitted, the recipient updates her beliefs and tells her team what option she currently believes to be best. This influence process continues until all agents prefer the same option. This option is the team’s decision.
The Agent-Based Model for Multiple Team Membership (ABMMTM) simulates design teams searching for viable design solutions, for a large design project that requires multiple design teams that are working simultaneously, under different organizational structures; specifically, the impact of multiple team membership (MTM). The key mechanism under study is how individual agent-level decision-making impacts macro-level project performance, specifically, wage cost. Each agent follows a stochastic learning approach, akin to simulated annealing or reinforcement learning, where they iteratively explore potential design solutions. The agent evaluates new solutions based on a random-walk exploration, accepting improvements while rejecting inferior designs. This iterative process simulates real-world problem-solving dynamics where designers refine solutions based on feedback.
As a proof-of-concept demonstration of assessing the macro-level effects of MTM in organizational design, we developed this agent-based simulation model which was used in a simulation experiment. The scenario is a system design project involving multiple interdependent teams of engineering designers. In this scenario, the required system design is split into three separate but interdependent systems, e.g., the design of a satellite could (trivially) be split into three components: power source, control system, and communication systems; each of three design team is in charge of a design of one of these components. A design team is responsible for ensuring its proposed component’s design meets the design requirement; they are not responsible for the design requirements of the other components. If the design of a given component does not affect the design requirements of the other components, we call this the uncoupled scenario; otherwise, it is a coupled scenario.
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 […]
This is a multi-patch meta-population ecological model. It intended as a test-bed in which to test the impact of humans with different kinds of social structure.
The purpose of this model is to examine equity and efficiency in crop production across a system of irrigated farms, as a function of maintenance costs, assessed water fees, and the capacity of farmers to trade water rights among themselves.
This model is to match students and schools using real-world student admission mechanisms. The mechanisms in this model are serial dictatorship, deferred acceptance, the Boston mechanism, Chinese Parallel, and the Taipei mechanism.
This Repast Simphony model simulates genomic admixture during the farming expansion of human groups from mainland Asia into the Papuan dominated islands of Southeast Asia during the Neolithic period.
The SWE models firms search behaviour as the performance landscape shifts. The shift represents society’s pricing of negative externalities, and the performance landscape is an NK structure. The model is written in NetLogo.
The model represents an archetypical fishery in a co-evolutionary social-ecological environment, capturing different dimensions of trust between fishers and fish buyers for the establishment and persistence of self-governance arrangements.
Our societal belief systems are pruned by evolution, informing our unsustainable economies. This is one of a series of models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, CmLab.
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