Decision Models
The Raw Sugarscape decision model represents the basic, unmodified behavior of agents as originally described in "Growing Artificial Societies" by Epstein and Axtell. Agents follow rules for movement, resource collection, and interaction without any advanced decision-making strategies. This model serves as a baseline for comparing the effects of more complex decision models.
The Bentham decision model is inspired by the utilitarian philosophy of Jeremy Bentham. Agents using this model aim to maximize their overall utility, which often includes considering future outcomes and balancing immediate gains with long-term benefits. This model introduces rational decision-making, simulating agents that prioritize actions leading to the greatest overall happiness or utility.
The Altruist decision model describes agents that prioritize the well-being of others over their own. These agents make decisions that benefit the community, even at a personal cost. This model is useful for exploring the dynamics of altruistic behavior and the emergence of social structures where agents work for mutual benefit.
The Egoist decision model characterizes agents that focus solely on their own benefit, disregarding the needs or welfare of others. These agents make decisions that maximize personal gain, often at the expense of others. This model helps in understanding the impact of selfish behavior on resource distribution, social interactions, and overall system stability.
This plot shows the population of agents over time for different decision models. It tracks how the population of agents changes at each timestep. Purpose: To visualize how different decision models affect the survival and reproduction of agents over time.
This plot displays the mean age at which agents die, tracked over time. Purpose: To compare the life expectancy of agents across various decision models and to observe trends in agent mortality over time.
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This plot illustrates the mean time to live for agents, showing how long agents are expected to live from birth to death across different timesteps. Purpose: To assess the impact of different decision models on the lifespan of agents, giving insights into how quickly agents reach the end of their life cycle.
This plot shows the total wealth accumulated by agents over time for each decision model. It tracks the economic performance of agents. Purpose: To analyze the economic success and resource accumulation of agents under different decision models.
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This plot normalizes the total wealth by dividing it by the population at each timestep, showing the average wealth per agent. Purpose: To provide a more nuanced view of wealth distribution by accounting for population changes, allowing for comparison of wealth per agent across decision models.
This plot shows the combined deaths due to starvation and combat as a fraction of the population over time. It tracks how many agents die from these specific causes. Purpose: To understand the mortality rate due to starvation and combat, providing insights into the dangers and challenges faced by agents under different decision models.