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Stochastic population and wealth trajectories for societies as functions of fertility are modeled with a minimal, endogenous model of a simple foraging economy. The model is scaled to match human lifespans and overall death rates. Stochastic population instability at both the high and low ends of fertility are considered. Lower population levels, caused by low fertility, generate concerns on economic growth, military security, and international political power; while also seen by some as reducing ecological and environmental damage. The model shows that increasingly low fertility leads to both higher wealth and lower population levels. As society is encouraged by increasing per capita wealth to continue to decrease fertility, dangerous population regimes are reached where stochastic extinction becomes more and more likely., Comment: 9 pages, 5 figures, 2 appendices, 1 table

Income inequality and redistribution policies are modeled with a minimal, endogenous model of a simple foraging economy. Significant income inequalities emerge from the model for populations of equally capable individuals presented with equal opportunities. Stochastic income distributions from the model are compared to empirical data from actual economies. The impacts of redistribution policies on total wealth, income distributions, and inequality are shown to be similar for the empirical data and the model. These comparisons enable detailed determinations of population welfare beyond what is possible with total wealth and inequality metrics. I, Comment: 12 pages, 5 figures, 1 table

Redistribution of resources within a group as a method to reduce wealth inequality is a current area of debate. The evolutionary path to or away from wealth sharing is also a subject of active research. In order to investigate effects and evolution of wealth sharing, societies are simulated using a minimal model of a complex adapting system. These simulations demonstrate, for this artificial foraging society, that local sharing of resources reduces the economy's total wealth and increases wealth inequality. Evolutionary pressures strongly select against local sharing, whether globally or within a individual's clan, and select for asocial behaviors. By holding constant the gene for sharing resources among neighbors, from rich to poor, either with everyone or only within members of the same clan, social behavior is selected but total wealth and mean age are substantially reduced relative to non-sharing societies. The Gini coefficient is shown to be ineffective in measuring these changes in total wealth and wealth distributions, and, therefore, individual well-being. Only with sociality do strategies emerge that allow sharing clans to exclude or coexist with non-sharing clans. These strategies are based on spatial effects, emphasizing the importance of modeling movement mediated community assembly and coexistence as well as sociality., Comment: 2 tables, 1 algorithm PDL, and 8 figures

Combining a spatiotemporal, multi-agent based model of a foraging ecosystem with linear, genetically programmed rules for the agents' behaviors results in implicit, endogenous, objective functions and selection algorithms based on "natural selection". Use of this implicit optimization of genetic programs for study of biological systems is tested by application to an artificial foraging ecosystem, and compared with established biological, ecological, and stochastic gene diffusion models. Limited program memory and execution time constraints emulate real-time and concurrent properties of physical and biological systems, and stress test the optimization algorithms. Relative fitness of the agents' programs and efficiency of the resultant populations as functions of these constraints gauge optimization effectiveness and efficiency. Novel solutions confirm the creativity of the optimization process and provide an unique opportunity to experimentally test the neutral code bloating hypotheses. Use of this implicit, endogenous, evolutionary optimization of spatially interacting, genetically programmed agents is thus shown to be novel, consistent with biological systems, and effective and efficient in discovering fit and novel solutions., Comment: 1 table, 4 figures, 1 PDL

The emergence of eusocial species is both very rare in evolutionary history and results in remarkably successful species. By inverting an agent based model, agent rules are discovered that display behaviors characteristic of eusocial species as well as other behaviors that lead to unexpected population dynamics. By holding the agents' genome constant across the colony and allowing the agents' rules to evolve, the individual behaviors exhibit phenotypic plasticity in response to environmental cues. The phenotypically driven reduction of intrinsic growth rates and the emergence of non-reproducing phenotypes both demonstrate selection pressure at the colony (system) level. The efficiency of an evolved colony is shown to have a strong relationship to the computational capacity of the agents. Various other emergent behaviors, both eusocial and otherwise novel, are identified and discussed. A path forward to more capable eusocial populations and inter-colony evolution is outlined., Comment: 20 pages, 9 figures, two tables, two appendices

Artificial ecosystems provide an additional experimental tool to support laboratory work, field work, and theoretical development in competitive exclusion research. A novel application of a spatiotemporal agent based model is presented which simulates two foraging species of different intrinsic growth rates competing for the same replenishing resource in stable and seasonal environments. This experimental approach provides precise control over the parameters for the environment, the species, and individual movements. Detailed trajectories of these non-equilibrium populations and their characteristics are produced. Narrow zones of potential coexistence are identified within the environmental and intrinsic growth parameter spaces. An example of commensalism driven by the local spatial dynamics is identified. Results of these experiments are discussed in context of modern coexistence theory and research in movement-mediated community assembly. Constraints on possible origination scenarios are identified., Comment: 10 pages, 10 figures, 1 table

A simple generative model of a foraging society generates significant wealth inequalities from identical agents on an equal opportunity landscape. These inequalities arise in both equilibrium and non-equilibrium regimes with some societies essentially never reaching equilibrium. Reproduction costs mitigate inequality beyond their affect on intrinsic growth rate. The highest levels of inequality are found during non-equilibrium regimes. Inequality in dynamic regimes is driven by factors different than those driving steady state inequality. Evolutionary pressures drive the intrinsic growth rate as high as possible, leading to a tragedy of the commons., Comment: 12 pages, 2 tables, 5 figures. Presented at the Social Simulation Conference 2021. arXiv admin note: substantial text overlap with arXiv:2101.09817

Single species population models and discrete stochastic gene frequency models are two standards of mathematical biology important for the evolution of populations. An agent based model is presented which reproduces these models and then explores where these models agree and disagree under relaxed specifications. For the population models, the requirement of homogeneous mixing prevents prediction of extinctions due to local resource depletion. These models also suggest equilibrium based on attainment of constant population levels though underlying population characteristics may be nowhere close to equilibrium. The discrete stochastic gene frequency models assume well mixed populations at constant levels. The models' predictions for non-constant populations in strongly oscillating and chaotic regimes are surprisingly good, only diverging from the ABM at the most chaotic levels., Comment: 10 pages, 5 figures. Presented at First International Workshop on Agentization, Rendering Conventional Models with Agent-Based Computing

While the use of spatial agent-based and individual-based models has flourished across many scientific disciplines, the complexities these models generate are often difficult to manage and quantify. This research reduces population-driven, spatial modeling of individuals to the simplest configurations and parameters: an equal resource opportunity landscape with equally capable individuals; and asks the question, "Will valid complex population and inequality dynamics emerge from this simple economic model?" Two foraging economies are modeled: subsistence and surplus. The resulting, emergent population dynamics are characterized by their sensitivities to agent and landscape parameters. The various steady and oscillating regimes of single-species population dynamics are generated by appropriate selection of model growth parameters. These emergent dynamics are shown to be consistent with the equation-based, continuum modeling of single-species populations in biology and ecology. The intrinsic growth rates, carry capacities, and delay parameters of these models are implied for these simple economies. Aggregate measures of individual distributions are used to understand the sensitivities to model parameters. New local measures are defined to describe complex behaviors driven by spatial effects, especially extinctions. This simple economic model is shown to generate significantly complex population and inequality dynamics. Model parameters generating the intrinsic growth rate have strong effects on these dynamics, including large variations in inequality. Significant inequality effects are shown to be caused by birth costs above and beyond their contribution to the intrinsic growth rate. The highest levels of inequality are found during the initial non-equilibrium period and are driven by factors different than those driving steady state inequality., Comment: 29 pages with 22 figures