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Edward D. Lee

Publications and source records attributed to Edward D. Lee.

At least 19 recordsLinked to original sources

Collective contributions to polarized voting

The crux of polarized voting in institutions like legislatures or courts is separation along a unidimensional ideological axis, but voting behavior is in reality more complex, with other signatures of collective order. We extend a foundational, statistical physics framework, restricted Boltzmann machines, to capture both such aspects of voting. The models are minimal, fit strongly correlated voting data, and have parameters that transparently give vote probabilities. The model accounts for multi-dimensional voter preferences and the context in which such preferences are expressed to disentangle individual from collective contributions; for example, legislative bills can negotiate multiple issues, whose appeal adds up or competes for individual votes. With the example of the U.S.~Senate, we find that senators have multi-dimensional preferences, and, as one consequence, non-polarized coalitions coexist with polarized ones. Increasing polarization is predominantly explained by fewer votes that elicit bipartisan coalitions. We show that these accounts can be consistent, if far more parsimonious, than interaction-driven order. The findings highlight the collective choice of the content and the rules of voting in the ebb and flow of polarization.

physics.soc-ph

Pulses, waves, and cascades in collective migration dynamics

Decisions to migrate depend on others' decisions. Dependence can produce nontrivial dynamics. We propose a minimal migration model that accounts for social influence alongside individual heterogeneity in mobility as migrants move from region to region. In special locations of parameter space, migrant flows dramatically and spontaneously fluctuate. Such aspects mimic observed fluctuations in migration statistics and thus show how large fluctuations in data can reflect more than response to events like armed conflict and natural disasters. Correspondingly, the impact of exogenous factors can be confounded with the results of collective decisions.

physics.soc-ph

AI sustains higher strategic tension than humans in chess

Strategic decision-making requires balancing immediate opportunities against long-term objectives: a tension fundamental to competitive environments. We investigate this trade-off in chess by analyzing the dynamics of human and AI gameplay through a network-based metric that quantifies piece-to-piece interactions. Our analysis reveals that elite AI players sustain substantially higher levels of strategic tension for longer durations than top human grandmasters. We find that cumulative tension scales with algorithmic complexity in AI systems and increases linearly with skill level (Elo rating) in human play. Longer time controls are associated with higher tension in human games, reflecting the additional strategic complexity players can manage with more thinking time. The temporal profiles reveal contrasting approaches: highly competitive AI systems tolerate densely interconnected positions that balance offensive and defensive tactics over extended periods, while human players systematically limit tension and game complexity. These differences have broader implications for understanding how artificial and biological systems navigate complex strategic environments and for the deployment of AI in high-stakes competitive scenarios.

cs.AI

Adjacent Possible Innovation Dynamics on Local Optima Networks

We propose Local Optima Networks (LONs) as a formal framework for modeling innovation dynamics. A LON is a directed weighted graph in which nodes represent locally stable technological configurations and edges encode transition probabilities between their basins of attraction. We construct LONs from fitness landscapes and model innovating agents as stochastic walkers exploring the adjacent possible on the resulting network. We show that this model simultaneously generates the four main empirical regularities of the discovery-process tradition: sublinear novelty growth (Heaps' law), heavy-tailed frequency distributions (Zipf's law), anomalous fluctuation scaling (Taylor's law), and power-law distributed inter-event times. The exponents fall within empirically observed ranges and are jointly constrained by LON topology. Communities in the LON provide an operational definition of technological paradigms grounded in basin-level accessibility. The LON framework thus bridges the discovery-process and adaptive-search traditions of innovation modeling within a single, parsimonious, and empirically testable representation.

physics.soc-ph

Synthesis of innovation and obsolescence

Innovation and obsolescence describe the dynamics of ever-churning social and biological systems, from the development of economic markets to scientific and technological progress to biological evolution. They have been widely discussed, but in isolation, leading to fragmented modeling of their dynamics. This poses a problem for connecting and building on what we know about their shared mechanisms. Here we collectively propose a conceptual and mathematical framework to transcend field boundaries and to explore unifying theoretical frameworks and open challenges. We ring an optimistic note for weaving together disparate threads with key ideas from the wide and largely disconnected literature by focusing on the duality of innovation and obsolescence and by proposing a mathematical framework to unify the metaphors between constitutive elements.

physics.soc-ph

Common indicators hurt armed conflict prediction

Are big conflicts different from small or medium size conflicts? To answer this question, we leverage fine-grained conflict data, which we map to climate, geography, infrastructure, economics, raw demographics, and demographic composition in Africa. With an unsupervised learning model, we find three overarching conflict types representing ``major unrest,'' ``local conflict,'' and ``sporadic and spillover events.'' Major unrest predominantly propagates around densely populated areas with well-developed infrastructure and flat, riparian geography. Local conflicts are in regions of median population density, are diverse socio-economically and geographically, and are often confined within country borders. Finally, sporadic and spillover conflicts remain small, often in low population density areas, with little infrastructure and poor economic conditions. The three types stratify into a hierarchy of factors that highlights population, infrastructure, economics, and geography, respectively, as the most discriminative indicators. Specifying conflict type negatively impacts the predictability of conflict intensity such as fatalities, conflict duration, and other measures of conflict size. The competitive effect is a general consequence of weak statistical dependence. Hence, we develop an empirical and bottom-up methodology to identify conflict types, knowledge of which can hurt predictability and cautions us about the limited utility of commonly available indicators.

physics.soc-ph

Innovation-exnovation dynamics on trees and trusses

Innovation and its complement exnovation describe the progression of realized possibilities from the past to the future, and the process depends on the structure of the underlying graph. For example, the phylogenetic tree represents the unique path of mutations to a single species. To a technology, paths are manifold, like a "truss." We solve for the phase diagram of a model, where a population innovates while outrunning exnovation. The dynamics progress on random graphs that capture the degree of historical contingency. Higher connectivity speeds innovation but also increases the risk of system collapse. We show how dynamics and structural connectivity conspire to unleash innovative diversity or to drive it extinct.

q-bio.PE

Information consumption and size in firms

Social and biological collectives need to exchange information to persist and to function. This happens across internal networks, whose structure represents static channels through which information flows. Less studied is the quantity and variety of information transmitted. We characterize a part of the information flow, the information going into organizations, primarily business firms. We measure what firms read using a data set of hundreds of millions of records of news articles accessed by employees across millions of firms. We measure and relate quantitatively three essential aspects: reading volume, reading variety, and firm size. First we compare volume with firm size, showing that firms grow sublinearly with the volume of their reading. The scaling means that inequality in information volume exaggerates the classic Zipf's law inequality in firm size, pointing to an economy of scale in information consumption. Then, by connecting variety and volume, we show that the firms vary in their reading habits to a limited degree. Firms above a certain size become repetitive readers, consistent with the sudden onset of a coordination cost between teams, not individual employees. Finally, we relate information variety to size to show that large firms tend to increase investments in existing areas of interest instead of divesting from them to move to new areas. We argue that this reflects structural constraints in growth. The results indicate how information consumption reflects the role of internal structure, beyond individual employees, analogous to information processing in other social and biological systems.

physics.soc-ph

Closely estimating the entropy of sparse graph models

We introduce an algorithm for estimating the entropy of pairwise, probabilistic graph models by leveraging bridges between social communities and an accurate entropy estimator on sparse samples. We propose using a measure of investment from the sociological literature, Burt's structural constraint, as a heuristic for identifying bridges that partition a graph into conditionally independent components. We combine this heuristic with the Nemenman-Shafee-Bialek entropy estimator to obtain a faster and more accurate estimator. We demonstrate it on the pairwise maximum entropy, or Ising, models of judicial voting, to improve naïve entropy estimates. We use our algorithm to estimate the partition function closely, which we then apply to the problem of model selection, where estimating the likelihood is difficult. This serves as an improvement over existing methods that rely on point correlation functions to test fit can be extended to other graph models with a straightforward modification of the open-source implementation.

physics.comp-ph

Outsourcing Memory Through Niche Construction

Adaptation to changing environments is a universal feature of life and can involve the organism modifying itself in response to the environment as well as actively modifying the environment to control selection pressures. The latter case couples the organism to environment. Then, how quickly should the organism change in response to the environment? We formulate this question in terms of how memory duration scales with environmental rate of change when there are trade-offs in remembering vs. forgetting. We derive a universal scaling law for optimal memory duration, taking into account memory precision as well as two components of environmental volatility, bias and stability. We find sublinear scaling with any amount of environmental volatility. We use a memory complexity measure to explore the strategic conditions (game dynamics) favoring actively reducing environmental volatility -- outsourcing memory through niche construction -- over investing in neural tissue. We predict stabilizing niche construction will evolve when neural tissue is costly, the environment is variable, and it is beneficial to be able to encode a rich repertoire of environmental states.

q-bio.PE

Idea engines: Unifying innovation and obsolescence from markets and genetic evolution to science

Innovation and obsolescence describe dynamics of ever-churning and adapting social and biological systems, concepts that encompass field-specific formulations. We formalize the connection with a reduced model of the dynamics of the "space of the possible" (e.g. technologies, mutations, theories) to which agents (e.g. firms, organisms, scientists) couple as they grow, die, and replicate. We predict three regimes: the space is finite, ever growing, or a Schumpeterian dystopia in which obsolescence drives the system to collapse. We reveal a critical boundary at which the space of the possible fluctuates dramatically in size, displaying recurrent periods of minimal and of veritable diversity. When the space is finite, corresponding to physically realizable systems, we find surprising structure. This structure predicts a taxonomy for the density of agents near and away from the innovative frontier that we compare with distributions of firm productivity, covid diversity, and citation rates for scientific publications. Remarkably, our minimal model derived from first principles aligns with empirical examples, implying a follow-the-leader dynamic in firm cost efficiency and biological evolution, whereas scientific progress reflects consensus that waits on old ideas to go obsolete. Our theory introduces a fresh and empirically testable framework for unifying innovation and obsolescence across fields.

physics.soc-ph

Discovering the mesoscale for chains of conflict

Conflicts, like many social processes, are related events that span multiple scales in time, from the instantaneous to multi-year developments, and in space, from one neighborhood to continents. Yet, there is little systematic work on connecting the multiple scales, formal treatment of causality between events, and measures of uncertainty for how events are related to one another. We develop a method for extracting related chains of events that addresses these limitations with armed conflict. Our method explicitly accounts for an adjustable spatial and temporal scale of interaction for clustering individual events from a detailed data set, the Armed Conflict Event & Location Data Project. With it, we discover a mesoscale ranging from a week to a few months and from tens to a few hundred kilometers, where long-range correlations and nontrivial dynamics relating conflict events emerge. Importantly, clusters in the mesoscale, while extracted only from conflict statistics, are identifiable with causal mechanism cited in field studies. We leverage our technique to identify zones of causal interaction around conflict hotspots that naturally incorporate uncertainties. Thus, we show how a systematic, data-driven procedure extracts social objects for study, providing a scope for scrutinizing and predicting conflict amongst other processes.

physics.soc-ph

Discovering sparse control strategies in C. elegans

Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology, and though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity but still characterizes collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations -- ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms -- to neural states and their impact on collective neural behavior. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, "pivotal" neurons that account for most of the system's sensitivity, suggesting a sparse mechanism for control of collective behavior.

q-bio.NC

Dynamics of growth, death, and resource competition in sessile organisms

Population-level scaling in ecological systems arises from individual growth and death with competitive constraints. We build on a minimal dynamical model of metabolic growth where the tension between individual growth and mortality determines population size distribution. We include resource competition based on shared capture area separately. By varying relative rates of growth, death, and competitive attrition, we connect regular and random spatial patterns across sessile organisms from forests to ants, termites, and fairy circles. Then, we consider transient temporal dynamics in the context of asymmetric competition that primarily weakens the smaller of two competitors such as canopy shading or large colony dominance. When such competition couples slow timescales of growth with fast competitive death, it generates population shock waves similar to those observed in forest demographic data. Our minimal quantitative theory unifies spatiotemporal patterns across sessile organisms through local competition mediated by the laws of metabolic growth which in turn result from long-term evolutionary dynamics.

q-bio.PE

Sensitivity of collective outcomes identifies pivotal components

A social system is susceptible to perturbation when its collective properties depend sensitively on a few pivotal components. Using the information geometry of minimal models from statistical physics, we develop an approach to identify pivotal components to which coarse-grained, or aggregate, properties are sensitive. As an example, we introduce our approach on a reduced toy model with a median voter who always votes in the majority. The sensitivity of majority-minority divisions to changing voter behaviour pinpoints the unique role of the median. More generally, the sensitivity identifies pivotal components that precisely determine collective outcomes generated by a complex network of interactions. Using perturbations to target pivotal components in the models, we analyse datasets from political voting, finance and Twitter. Across these systems, we find remarkable variety, from systems dominated by a median-like component to those whose components behave more equally. In the context of political institutions such as courts or legislatures, our methodology can help describe how changes in voters map to new collective voting outcomes. For economic indices, differing system response reflects varying fiscal conditions across time. Thus, our information-geometric approach provides a principled, quantitative framework that may help assess the robustness of collective outcomes to targeted perturbation and compare social institutions, or even biological networks, with one another and across time.

physics.soc-ph

Emergent regularities and scaling in armed conflict data

Armed conflict exhibits regularities beyond known power law distributions of fatalities and duration over varying culture and geography. We systematically cluster conflict reports from a database of $10^5$ events from Africa spanning 20 years into conflict avalanches. Conflict profiles collapse over a range of scales. Duration, diameter, extent, fatalities, and report totals satisfy mutually consistent scaling relations captured with a model combining geographic spread and local conflict-site growth. The emergence of such social scaling laws hints at principles guiding conflict evolution.

physics.soc-ph

A scaling theory of armed conflict avalanches

Armed conflict data display scaling and universal dynamics in both social and physical properties like fatalities and geographic extent. We propose a randomly branching, armed-conflict model that relates multiple properties to one another in a way consistent with data. The model incorporates a fractal lattice on which conflict spreads, uniform dynamics driving conflict growth, and regional virulence that modulates local conflict intensity. The quantitative constraints on scaling and universal dynamics we use to develop our minimal model serve more generally as a set of constraints for other models for armed conflict dynamics. We show how this approach akin to thermodynamics imparts mechanistic intuition and unifies multiple conflict properties, giving insight into causation, prediction, and intervention timing.

physics.soc-ph

Convenient Interface to Inverse Ising (ConIII): A Python 3 Package for Solving Ising-Type Maximum Entropy Models

ConIII (pronounced CON-ee) is an open-source Python project providing a simple interface to solving the pairwise and higher order Ising model and a base for extension to other maximum entropy models. We describe the maximum entropy problem and give an overview of the algorithms that are implemented as part of ConIII (https://github.com/eltrompetero/coniii) including Monte Carlo histogram, pseudolikelihood, minimum probability flow, a regularized mean field method, and a cluster expansion method. Our goal is to make a variety of maximum entropy techniques accessible to those unfamiliar with the techniques and accelerate workflow for users.

q-bio.QM