The simulation model LAMDA investigates the influences of varying cognitive abilities of the decision maker on the truth-inducing effect of the Groves mechanism. Bounded rationality concepts are represented by information states and learning models.

This ABM deals with commuting choices in the Italian city of Varese. Empirical data inform agents’ attitudes and modal choices costs and emissions. We evaluate ex ante the impact of policies for less polluting commuting choices.

The agent-based model captures the spatio-temporal institutional dynamics of the economy over the years at the level of a Dutch province. After 1945, Noord-Brabant in the Netherlands has been subject to an active program of economic development through the stimulation of pig husbandry. This has had far-reaching effects on its economy, landscape, and environment. The agents are households. The simulation is at institutional level, with typical stakeholder groups, lobbies, and political parties playing a role in determining policies that in turn determine economic, spatial and ecological outcomes. It allows to experiment with alternative scenarios based on two political dimensions: local versus global issues, and economic versus social responsibilitypriorities. The model shows very strong sensitivity to political context. It can serve as a reference model for other cases where “artificial institutional economics” is attempted.

This version 2.1.0 of the uFunk model is about setting a business strategy (the S in the name) for an organization. A team of managers (or executives) meet and discuss various options on the strategy for the firm. There are three aspects that they have to agree on to set the strategic positioning of the organization.
The discussion is on market, stakeholders, and resources. The team (it could be a business strategy task force) considers various aspects of these three elements. The resources they use to develop the discussion can come from a traditional approach to strategy or from non-traditional means (e.g., so-called serious play, creativity and imagination techniques).
The S-uFunk 2.1.0 Model wants to understand to which extent cognitive means triggered by traditional and non-traditional resources affect the making of the strategy process.

A minimal genetic algorithm was previously developed in order to solve an elementary arithmetic problem. It has been modified to explore the effect of a mutator gene and the consequent entrance into a hypermutation state. The phenomenon seems relevant in some types of tumorigenesis and in a more general way, in cells and tissues submitted to chronic sublethal environmental or genomic stress.
For a long time, some scholars suppose that organisms speed up their own evolution by varying mutation rate, but evolutionary biologists are not convinced that evolution can select a mechanism promoting more (often harmful) mutations looking forward to an environmental challenge.
The model aims to shed light on these controversial points of view and it provides also the features required to check the role of sex and genetic recombination in the mutator genes diffusion.

Previous research on organizations often focuses on either the individual, team, or organizational level. There is a lack of multidimensional research on emergent phenomena and interactions between the mechanisms at different levels. This paper takes a multifaceted perspective on individual learning and autonomous group formation and turnover. To analyze interactions between the two levels, we introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. To solve a task, agents form a group that can be adapted from time to time. We explore organizations that promote learning and group turnover either simultaneously or sequentially and analyze the interactions between the activities and the effects on performance. We observe underproportional interactions when tasks are interdependent and show that pushing learning and group turnover too far might backfire and decrease performance significantly.

This model takes concepts from a JASSS paper this is accepted for the October, 2023 edition and applies the concepts to empirical data from counties surrounding and including Cleveland Ohio. The agent-based model has a proportional number of agents in each of the counties to represent the correct proportions of adults in these counties. The adoption decision probability uses the equations from Bass (1969) as adapted by Rand & Rust (2011). It also includes the Outgroup aversion factor from Smaldino, who initially had used a different imitation model on line grid. This model uses preferential attachment network as a metaphor for social networks influencing adoption. The preferential network can be adjusted in the model to be created based on both nodes preferred due to higher rank as well as a mild preference for nodes of a like group.

We propose here a computational model of school segregation that is aligned with a corresponding Schelling-type model of residential segregation. To adapt the model for application to school segregation, we move beyond previous work by combining two preference arguments in modeling parents’ school choice, preferences for the ethnic composition of a school and preferences for minimizing the travelling distance to the school.

In a two-level hierarchical structure (consisting of the positions of managers and operators), persons holding these positions have a certain performance and the value of their own (personal perception in this, simplified, version of the model) perception of each other. The value of the perception of each other by agents is defined as a random variable that has a normal distribution (distribution parameters are set by the control elements of the interface).
In the world of the model, which is the space of perceptions, agents implement two strategies: rapprochement with agents that perceive positively and distance from agents that perceive negatively (both can be implemented, one of these strategies, or neither, the other strategy, which makes the agent stationary). Strategies are implemented in relation to those agents that are in the radius of perception (PerRadius).
The manager (Head) forms a team of agents. The performance of the group (the sum of the individual productivities of subordinates, weighted by the distance from the leader) varies depending on the position of the agents in space and the values of their individual productivities. Individual productivities, in the current version of the model, are set as a random variable distributed evenly on a numerical segment from 0 to 100. The manager forms the team 1) from agents that are in (organizational) radius (Op_Radius), 2) among agents that the manager perceives positively and / or negatively (both can be implemented, one of the specified rules, or neither, which means the refusal of the command formation).
Agents can (with a certain probability, given by the variable PrbltyOfDecisn%), in case of a negative perception of the manager, leave his group permanently.
It is possible in the model to change on the fly radii values, update the perception value across the entire population and the perception of an individual agent by its neighbors within the perception radius, and the probability values for a subordinate to make a decision about leaving the group.
You can also change the set of strategies for moving agents and strategies for recruiting a team manager. It is possible to add a randomness factor to the movement of agents (Stoch_Motion_Speed, the default is set to 0, that is, there are no random movements).
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The model explores how corruption may spread endogenously within a closed society by depicting the behavior within a cellular automaton context (CA) between bureaucrats and citizens. Within the model, corruption is characterized as a behavior product dependent upon an individual’s personal disposition towards honesty, rational decisionmaking processes, and neighbors’ behavior.