The purpose of the study is to unpack and explore a potentially beneficial role of sharing metacognitive information within a group when making repeated decisions about common pool resource (CPR) use.

We explore the explanatory power of sharing metacognition by varying (a) the individual errors in judgement (myside-bias); (b) the ways of reaching a collective judgement (metacognition-dependent), (c) individual knowledge updating (metacognition- dependent) and d) the decision making context.

The model (AgentEx-Meta) represents an extension to an existing and validated model reflecting behavioural CPR laboratory experiments (Schill, Lindahl & Crépin, 2015; Lindahl, Crépin & Schill, 2016). AgentEx-Meta allows us to systematically vary the extent to which metacognitive information is available to agents, and to explore the boundary conditions of group benefits of metacognitive information.

An agent-based model that simulates urban neighbourhoods. The model has been designed to simulate perceived livability and safety (PLS) of citizens. The score attached to perceived livability and safety, PLS, is the main output of the model and is the average of each individual’s PLS. These PLS scores, in turn, are specific to each citizen and highly dependent on their individual experiences. PLS is impacted by several different social factors: interactions with fellow citizens, police officers, and community workers; visiting or starting a neighbourhood initiative; experiencing a burglary; seeing a youth gang; or hearing from friends (of friends) about these events. On top of this, the model allows to set various types of social networks which also influence the PLS.

Digital-Twin model of Sejong City – Source model code & data

We only shared model codes, excluding private data and simulation engine codes.
The followings are brief reasons for the items we cannot share.

1. Residence address data
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We present the Integrated Urban Complexity model (IUCm 1.0) that computes “climate-smart urban forms”, which are able to cut emissions related to energy consumption from urban mobility in half. Furthermore, we show the complex features that go beyond the normal debates about urban sprawl vs. compactness. Our results show how to reinforce fractal hierarchies and population density clusters within climate risk constraints to significantly decrease the energy consumption of urban mobility. The new model that we present aims to produce new advice about how cities can combat climate change. From a technical angle, this model is a geographical automaton, conceptually interfacing between cellular automata and spatial explicit optimisation to achieve normative sustainability goals related to low energy. See a complete user guide at https://iucm.readthedocs.io/en/latest/ .

NOMAD is an agent-based model of firm location choice between two aggregate regions (“near” and “off”) under logistics uncertainty. Firms occupy sites characterised by attractiveness and logistics risk, earn a risk-adjusted payoff that depends on regional costs (wages plus congestion) and an individual risk-tolerance trait, and update location choices using aspiration-based satisficing rules with switching frictions. Logistics risk evolves endogenously on occupied sites through a region-specific absorption mechanism (good/bad events that reduce/increase risk), while congestion feeds back into regional costs via regional shares and local crowding. Runs stop endogenously once the near-region share becomes quasi-stable after burn-in, and the model records time series and quasi-stable outcomes such as near/off composition, switching intensity, costs, average risk, and average risk tolerance.

The simulation model SimPLS shows an application of the PLS agent concept, using SEM as empirical basis for the definition of agent architectures. The simulation model implements the PLS path model TAM about the decision of using innovative products.

CROwd Simulation of Situated individuals represents a modern generation simulation as a (social) scientific tool for understanding crowd behaviour. The CROSS model represents individuals in a crowd as social-cognitive agents that are affected by their social and physical surroundings and produce behaviour and behaviour patterns.

A series of studies show the applicability of the NK model in the crowdsourcing research, but it also exposes a problem that the application of the NK model is not tightly integrated with crowdsourcing process, which leads to lack of a basic crowdsourcing simulation model. Accordingly, by introducing interaction relationship among task decisions to define three tasks of different structure: local task, small-world task and random task, and introducing bounded rationality and its two dimensions are taken into account: bounded rationality level that used to distinguish industry types and bounded rationality bias that used to differentiate professional users and ordinary users, an agent-based model that simulates the problem-solving process of tournament-based crowdsourcing is constructed by combining the NK fitness landscapes and the crowdsourcing framework of “Task-Crowd-Process-Evaluation”.

The Emergent Firm (EF) model is based on the premise that firms arise out of individuals choosing to work together to advantage themselves of the benefits of returns-to-scale and coordination. The Emergent Firm (EF) model is a new implementation and extension of Rob Axtell’s Endogenous Dynamics of Multi-Agent Firms model. Like the Axtell model, the EF model describes how economies, composed of firms, form and evolve out of the utility maximizing activity on the part of individual agents. The EF model includes a cash-in-advance constraint on agents changing employment, as well as a universal credit-creating lender to explore how costs and access to capital affect the emergent economy and its macroeconomic characteristics such as firm size distributions, wealth, debt, wages and productivity.

A series of studies show the applicability of the NK model in the crowdsourcing research, but it also exposes a problem that the application of the NK model is not tightly integrated with crowdsourcing process, which leads to lack of a basic crowdsourcing simulation model. Accordingly, by introducing interaction relationship among task decisions to define three tasks of different structure: local task, small-world task and random task, and introducing bounded rationality and its two dimensions are taken into account: bounded rationality level that used to distinguish industry types and bounded rationality bias that used to differentiate professional users and ordinary users, an agent-based model that simulates the problem-solving process of tournament-based crowdsourcing is constructed by combining the NK fitness landscapes and the crowdsourcing framework of “Task-Crowd-Process-Evaluation”.