HOW IT WORKS

This model consists of three agents, and each agent type operates per business theories as below.
a. New technologies(Tech): It evolves per sustaining or disruptive technology trajectory with the constraint of project management triangle (Scope, Time, Quality, and Cost).
b. Entrepreneurs(Entre): It builds up the solution by combining Tech components per its own strategy (Exploration, Exploitation, or Ambidex).
c. Consumer(Consumer): It selects the solution per its own preference due to Diffusion of innovation theory (Innovators, Early Adopters, Early Majority, Late Majority, Laggards)
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The Urban Traffic Simulator is an agent-based model developed in the Unity platform. The model allows the user to simulate several autonomous vehicles (AVs) and tune granular parameters such as vehicle downforce, adherence to speed limits, top speed in mph and mass. The model allows researchers to tune these parameters, run the simulator for a given period and export data from the model for analysis (an example is provided in Jupyter Notebook).

The data the model is currently able to output are the following:

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The purpose of the model is to collect information on human decision-making in the context of coalition formation games. The model uses a human-in-the-loop approach, and a single human is involved in each trial. All other agents are controlled by the ABMSCORE algorithm (Vernon-Bido and Collins 2020), which is an extension of the algorithm created by Collins and Frydenlund (2018). The glove game, a standard cooperative game, is used as the model scenario.

The intent of the game is to collection information on the human players behavior and how that compares to the computerized agents behavior. The final coalition structure of the game is compared to an ideal output (the core of the games).

FIBE represents a simple fishery model. Fish that reproduce and fisher with different fishing styles that fish as their main source of income. The aim of the model is to reflect the different fishing behaviours as described and observed in the (Swedish) Baltic Sea fishery and explore the consequences of different approximations of human/fisher behaviour in under different environmental and managerial scenarios.

The overarching aim is to advance the incorporation and understanding of human behaviour (diversity) in fisheries research and management. In particular focusing on insights from social (fishery) science of fisher behaviour.

This model simulates the propagation of photons in a water tank. A source of light emits an impulse of photons with equal energy represented by yellow dots. These photons are then scattered by water particles before possibly reaching the photo-detector represented by a gray line. Different types of water are considered. For each one of them we calculate the total received energy.

The water tank is represented by a blue rectangle with fixed dimensions. It’s exposed to the air interface and has totally absorbent barriers. Four types of water are supported. Each one is characterized by its absorption and scattering coefficients.
At the source, the photons are generated uniformly with a random direction within the beamwidth. Each photon travels a random distance drawn from a distribution depending on the water characteristics before encountering a water particle.
Based on the updated position of the photon, three situations may occur:
-The photon hits the barrier of the tank on its trajectory. In this case it’s considered as lost since the barriers are assumed totally absorbent.
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Developed as a part of a project in the University of Augsburg, Institute of Geography, it simulates the traffic in an intersection or junction which uses either regular traffic lights or traffic lights with a countdown timer. The model tracks the average speed of cars before and after traffic lights as well as the throughput.

This proof-of-concept model explores the effects of how social and natural factors are incorporated (factor configuration) in environmentally induced migration. It is built in a conceptual environment where five regions are located in a row.

Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.

In macroeconomics, an emerging discussion of alternative monetary systems addresses the dimensions of systemic risk in advanced financial systems. Monetary regime changes with the aim of achieving a more sustainable financial system have already been discussed in several European parliaments and were the subject of a referendum in Switzerland. However, their effectiveness and efficacy concerning macro-financial stability are not well-known. This paper introduces a macroeconomic agent-based model (MABM) in a novel simulation environment to simulate the current monetary system, which may serve as a basis to implement and analyze monetary regime shifts. In this context, the monetary system affects the lending potential of banks and might impact the dynamics of financial crises. MABMs are predestined to replicate emergent financial crisis dynamics, analyze institutional changes within a financial system, and thus measure macro-financial stability. The used simulation environment makes the model more accessible and facilitates exploring the impact of different hypotheses and mechanisms in a less complex way. The model replicates a wide range of stylized economic facts, including simplifying assumptions to reduce model complexity.

This model aims to examine how different levels of communication noise and superiority bias affect team performance when solving problems collectively. We used a networked agent-based model of collective problem solving in which agents explore the NK landscape for a better solution and communicate with each other regarding their current solutions. We compared the team performance in solving problems collectively at different levels of self-superiority bias when facing simple and complex problems. Additionally, we addressed the effect of different levels of communication noise on the team’s outcome