arXiv subjects

Jordan T Kemp

Publications and source records attributed to Jordan T Kemp.

4 recordsLinked to original sources

arXiv2025

Spatial Selection and the Multiscale Dynamics of Urban Change

Growth is a multi-layered phenomenon in human societies, composed of socioeconomic and demographic change at many different scales. Yet, standard macroeconomic indicators average over most of these processes, blurring the spatial and hierarchical heterogeneity driving people's choices and experiences. To address this gap, we introduce here a framework based on the Price equation to decompose aggregate growth exactly into endogenous and selection effects across nested spatial scales. We illustrate this approach with population and income data from the Chicago metropolitan area (2014-2019) and show that both growth rates and spatial selection effects are most intense at local levels, fat-tailed and spatially correlated. We also find that selection, defined as the covariance between prevailing income and relative population change, is concentrated in few spatial units and exhibits scaling behavior when grouped by county. Despite the intensity of local sorting, selection effects largely cancel in the aggregate, implying that fast heterogeneous micro-dynamics can yield deceptively stable macro-trends. By treating local spatial units (neighborhoods) as evolving subpopulations under selection, we demonstrate how methods from complex systems provide new tools to classify residential selection processes, such as abandonment and gentrification, in an urban sociological framework. This approach is general and applies to any other nested economic systems such as networks of production, occupations, or innovation enabling a new mechanistic understanding of compositional change and growth across scales of organization.

physics.soc-ph↗

arXiv2025

Redefining Fitness: Inference, Information and Phase Transitions in Evolutionary Dynamics

Evolution is the adaptation of populations to their environment expressed through the concept of fitness. Darwin did not define fitness but described evolution as the higher prevalence of lineages with advantages in survival and reproduction in changing environments, an implicitly statistical and relational notion. As evolutionary dynamics became more quantitative, however, fitness acquired a narrower meaning of relative reproductive success. Crucially, this narrower definition suffers from three fundamental difficulties, known as the circularity, mismatch, and prediction problems. We show that interpreting evolutionary dynamics in terms of inference resolves these three problems while also creating new productive analytical tools. This shift redefines fitness via a Bayesian likelihood, a predictive probability of the environment specific to each type. We show that averaging the growth rate over environmental histories connects selection to information as types with better environmental models are amplified. It follows that long-run evolutionary dynamics maximizes the mutual information between population structure and environmental statistics, establishing information maximization as the governing principle of natural selection. We illustrate this approach in several population dynamics problems including task switching, evolutionary games, and selection in group-structured populations. In each case, we derive phase diagrams as functions of environmental statistics and Hamilton-type rules for the emergence of cooperation, while also demonstrating the generality of the approach.

q-bio.PE↗

arXiv2023

Information Synergy Maximizes the Growth Rate of Heterogeneous Groups

Collective action and group formation are fundamental behaviors among both organisms cooperating to maximize their fitness, and people forming socioeconomic organizations. Researchers have extensively explored social interaction structures via game theory and homophilic linkages, such as kin selection and scalar stress, to understand emergent cooperation in complex systems. However, we still lack a general theory capable of predicting how agents benefit from heterogeneous preferences, joint information, or skill complementarities in statistical environments. Here, we derive general statistical dynamics for the origin of cooperation based on the management of resources and pooled information. Specifically, we show how groups that optimally combine complementary agent knowledge about resources in statistical environments maximize their growth rate. We show that these advantages are quantified by the information synergy embedded in the conditional probability of environmental states given agents' signals, such that groups with a greater diversity of signals maximize their collective information. It follows that, when constraints are placed on group formation, agents must intelligently select with whom they cooperate to maximize the synergy available to their own signal. Our results show how the general properties of information underlie the optimal collective formation and dynamics of groups of heterogeneous agents across social and biological phenomena.

physics.soc-ph↗

arXiv2023

Stochastic Pairwise Preference Convergence in Bayesian Agents

Beliefs inform the behavior of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to explain how preferences evolve in cases of simple agent interactions. In this paper, we derive a Gaussian, pairwise agent interaction model to study how preferences converge when driven by observation of each other's behaviors. We show that the dynamics of convergence resemble an Ornstein-Uhlenbeck process, a common model in nonequilibrium stochastic dynamics. Using standard analytical and computational techniques, we find that the hyperprior magnitudes, representing the learning time, determine the convergence value and the asymptotic entropy of the preferences across pairs of agents. We also show that the dynamical variance in preferences is characterized by a relaxation time $t^\star$, and compute its asymptotic upper bound. This formulation enhances the existing toolkit for modeling stochastic, interactive agents by formalizing leading theories in learning theory, and builds towards more comprehensive models of open problems in principal-agent and market theory.

nlin.AO↗