PolicySpace models public policies within an empirical, spatial environment using data from 46 metropolitan regions in Brazil. The model contains citizens, markets, residences, municipalities, commuting and a the tax scheme. In the associated publications (book in press and https://arxiv.org/abs/1801.00259) we validate the model and demonstrate an application of the fiscal analysis. Besides providing the basics of the platform, our results indicate the relevance of the rules of taxes transfer for cities’ quality of life.

This adaptation of the Relative Agreement model of opinion dynamics (Deffuant et al. 2002) extends the Meadows and Cliff (2012) implementation of this model in a manner that explores the effect of the network structure among the agents.

The purpose of the model is to examine whether and how mobile pastoralists are able to achieve an Ideal Free Distribution (IFD).

The St Anthony flu model is an epidemiological model designed to test hypotheses related to the spread of the 1918 influenza pandemic among residents of a small fishing community in Newfoundland and Labrador. The 1921 census data from Newfoundland and Labrador are used to ensure a realistic model population; the community of St. Anthony, NL, located on the tip of the Northern Peninsula of the island of Newfoundland is the specific population modeled. Model agents are placed on a map-like grid that consists of houses, two churches, a school, an orphanage, a hospital, and several boats. They engage in daily activities that reflect known ethnographic patterns of behavior in St. Anthony and other similar communities. A pathogen is introduced into the community and then it spreads throughout the population as a consequence of individual agent movements and interactions.

The purpose of the simulation was to explore and better understand the process of bridging between an analysis of qualitative data and the specification of a simulation. This may be developed for more serious processes later but at the moment it is merely an illustration.
This exercise was done by Stephanie Dornschneider (School of Politics and International Relations, University College Dublin) and Bruce Edmonds to inform the discussion at the Lorentz workshop on “Integrating Qualitative and Quantitative Data using Social Simulation” at Leiden in April 2019. The qualitative data was collected and analysed by SD. The model specification was developed as the result of discussion by BE & SD. The model was programmed by BE. This is described in a paper submitted to Social Simulation 2019 and (to some extent) in the slides presented at the workshop.

The wisdom of the crowd refers to the phenomenon in which a group of individuals, each making independent decisions, can collectively arrive at highly accurate solutions—often more accurate than any individual within the group. This principle relies heavily on independence: if individual opinions are unbiased and uncorrelated, their errors tend to cancel out when averaged, reducing overall bias. However, in real-world social networks, individuals are often influenced by their neighbors, introducing correlations between decisions. Such social influence can amplify biases, disrupting the benefits of independent voting. This trade-off between independence and interdependence has striking parallels to ensemble learning methods in machine learning. Bagging (bootstrap aggregating) improves classification performance by combining independently trained weak learners, reducing bias. Boosting, on the other hand, explicitly introduces sequential dependence among learners, where each learner focuses on correcting the errors of its predecessors. This process can reinforce biases present in the data even if it reduces variance. Here, we introduce a new meta-algorithm, casting, which captures this biological and computational trade-off. Casting forms partially connected groups (“castes”) of weak learners that are internally linked through boosting, while the castes themselves remain independent and are aggregated using bagging. This creates a continuum between full independence (i.e., bagging) and full dependence (i.e., boosting). This method allows for the testing of model capabilities across values of the hyperparameter which controls connectedness. We specifically investigate classification tasks, but the method can be used for regression tasks as well. Ultimately, casting can provide insights for how real systems contend with classification problems.

Subjective biases and errors systematically affect market equilibria, whether at the population level or in bilateral trading. Here, we consider the possibility that an agent engaged in bilateral trading is mistaken about her own valuation of the good she expects to trade, that has not been explicitly incorporated into the existing bilateral trade literature. Although it may sound paradoxical that a subjective private valuation is something an agent can be mistaken about, as it is up to her to fix it, we consider the case in which that agent, seller or buyer, consciously or not, given the structure of a market, a type of good, and a temporary lack of information, may arrive at an erroneous valuation. The typical context through which this possibility may arise is in relation with so-called experience goods, which are sold while all their intrinsic qualities are still unknown (such as untasted bottled fine wines). We model this “private misvaluation” phenomenon in our study. The agents may also be mistaken about how their exchange counterparties are themselves mistaken. Formally, they attribute a certain margin of error to the other agent, which can differ from the actual way that another agent misvalues the good under consideration. This can constitute the source of a second-order misvaluation. We model different attitudes and situations in which agents face unexpected signals from their counterparties and the manner and extent to which they revise their initial beliefs. We analyse and simulate numerically the consequences of first-order and second-order misvaluation on market equilibria.

This is a model of innovation implementation inside an organization. It characterizes an innovation as a set of distributed and technically interdependent tasks performed by a number of different and socially interconnected frontline workers.

Risk assessments are designed to measure cumulative risk and promotive factors for delinquency and recidivism, and are used by criminal and juvenile justice systems to inform sanctions and interventions. Yet, these risk assessments tend to focus on individual risk and often fail to capture each individual’s environmental risk. This agent-based model (ABM) explores the interaction of individual and environmental risk on the youth. The ABM is based on an interactional theory of delinquency and moves beyond more traditional statistical approaches used to study delinquency that tend to rely on point-in-time measures, and to focus on exploring the dynamics and processes that evolve from interactions between agents (i.e., youths) and their environments. Our ABM simulates a youth’s day, where they spend time in schools, their neighborhoods, and families. The youth has proclivities for engaging in prosocial or antisocial behaviors, and their environments have likelihoods of presenting prosocial or antisocial opportunities.

CINCH1 (Covid-19 INfection Control in Hospitals), is a prototype model of physical distancing for infection control among staff in University College London Hospital during the Covid-19 pandemic, developed at the University of Leeds, School of Geography. It models the movement of collections of agents in simple spaces under conflicting motivations of reaching their destination, maintaining physical distance from each other, and walking together with a companion. The model incorporates aspects of the Capability, Opportunity and Motivation of Behaviour (COM-B) Behaviour Change Framework developed at University College London Centre for Behaviour Change, and is aimed at informing decisions about behavioural interventions in hospital and other workplace settings during this and possible future outbreaks of highly contagious diseases. CINCH1 was developed as part of the SAFER (SARS-CoV-2 Acquisition in Frontline Health Care Workers – Evaluation to Inform Response) project
(https://www.ucl.ac.uk/behaviour-change/research/safer-sars-cov-2-acquisition-frontline-health-care-workers-evaluation-inform-response), funded by the UK Medical Research Council. It is written in Python 3.8, and built upon Mesa version 0.8.7 (copyright 2020 Project Mesa Team).