arXiv subjects

Vicky Chuqiao Yang

Publications and source records attributed to Vicky Chuqiao Yang.

17 recordsLinked to original sources

arXiv2026

Social learning drives underprioritization of collective challenges

Societies often struggle to prioritize important challenges in a timely manner, with substantial costs from delayed action on issues like climate change and pandemic mitigation. A persistent puzzle is that broad concern on issues often fails to translate into collective priority. We argue that a key driver lies in how concern is formed across competing issue domains. Some issues depend heavily on social learning, where individuals infer importance from others, often because direct experience is limited. Others depend more on individual learning from firsthand experience. We develop a dynamic model in which two subgroups form issue-specific concerns through individual and social learning, and these concerns are aggregated into collective priority. The model yields three insights. First, with two issues of equal objective severity, the one that depends more on social learning tends to be underprioritized when both issues are severe. Second, gradual increases in severity delay reprioritization of the issue, with the delay growing as reliance on social learning increases. Third, this bias can be reduced by reducing social learning or by increasing intergroup learning beyond a critical threshold. These results offer a general mechanism for why severe problems can remain neglected in collective action despite widespread concern, and why intergroup interaction or experiential simulations may help align collective priorities with objective risks.

physics.soc-ph↗

arXiv2026

What Leads to Administrative Bloat? A Dynamic Model of Administrative Cost and Waste

The functioning of complex systems depends on the coordination of diverse components, often supported by regulatory structures that incur costs. In human organizations, such costs manifest as administrative burden, which has been rising despite often reducing efficiency. Classic explanations point to bureaucrat self-interest or regulation, yet they do not explain variation across organizations or clarify how this burden can be reduced. Here, we develop a dynamical model of administrative growth that integrates known behavioral mechanisms of process creation, obsolescence, and removal. The model conceptualizes processes as developed for problem solving, but becoming obsolete as conditions change, while continuing to consume resources until actively pruned. This interplay generates two long-term outcomes: stable equilibrium or run-away growth. The threshold separating these outcomes is shaped by organizations' propensity to create new processes when faced with problems, and their propensity to prune obsolete ones in response to administrative burden. Importantly, their effects are asymmetric: sufficiently high creation propensity leads to bloat regardless of pruning propensity. Faster environmental change shifts this threshold, making bloat more likely. Simulations of interventions show that lasting reductions in administrative costs and waste require permanent shifts in priorities and investments in distinguishing obsolete from useful processes. Temporary efforts or indiscriminate cuts provide only short-lived relief, and counterintuitively, prioritizing direct production can increase waste. Our work highlights a general mechanism by which well-intentioned problem-solving can create self-reinforcing inefficiencies in complex systems, offering insights possibly generalizable to broader applications, such as legal, policy, and software systems where obsolete elements accumulate.

physics.soc-ph↗

arXiv2026

Scaling Laws for Function Diversity and Specialization Across Socioeconomic and Biological Complex Systems

Function diversity, the range of tasks individuals perform, and specialization, the distribution of function abundances, are fundamental to complex adaptive systems. In the absence of overarching principles, these properties have appeared domain-specific. Here, we introduce an empirical framework and a mathematical model for the diversification and specialization of functions across disparate systems, including bacteria, federal agencies, universities, corporations, and cities. We find that the number of functions grows sublinearly with system size, with exponents from 0.35 to 0.57, consistent with Heaps' Law. In contrast, cities exhibit logarithmic scaling. To explain these empirical findings, we generalize the Yule-Simon model by introducing two key parameters: a diversification parameter that characterizes how existing functions inhibit the creation of new ones, and a specialization parameter that describes how a function's attractiveness depends on its abundance. Our model enables cross-system comparisons, from microorganisms to metropolitan areas. The analysis suggests that what drives the creation of new functions depends on the system's goals and structure: federal agencies tend to ensure comprehensive coverage of necessary functions; cities tend to slow the creation of new occupations as existing ones expand; and cells occupy an intermediate position. Once functions are introduced, their growth follows a remarkably universal pattern across all systems.

physics.soc-ph↗

arXiv2025

A generative model of function growth explains hidden self-similarities across biological and social systems

From genomes and ecosystems to bureaucracies and cities, the growth of complex systems occurs by adding new types of functions and expanding existing ones. We present a simple generative model that generalizes the Yule-Simon process by including: (i) a size-dependent probability of introducing new functions, and (ii) a generalized preferential attachment mechanism for expanding existing ones. We uncover a shared underlying structure that helps explain how function diversity evolves in empirical observations, such as prokaryotic proteomes, U.S. federal agencies, and urban economies. We show that real systems are often best represented as having non-Zipfian rank-frequency distributions, driven by sublinear preferential attachment, whilst still maintaining power-law scaling in their abundance distributions. Furthermore, our analytics explain five distinct phases of the organization of functional elements across complex systems. The model integrates empirical findings regarding the logarithmic growth of diversity in cities and the self-similarity of their rank-frequency distributions. Self-similarity previously observed in the rank-frequency distributions of cities is not observed in cells and federal agencies -- however, under a rescaling relative to the total diversity, all systems admit self-similar structures predicted by our theory.

physics.soc-ph↗

arXiv2025

How much regulation do we need from genomes to society?

Regulatory functions are essential in both socioeconomic and biological systems, from corporate managers to regulatory genes. Regulatory functions come with substantial costs and benefits, and the balance of the two is often taken for granted. A fundamental question for all complex systems becomes how much regulatory function do they need for their size and function? Here, we present empirical evidence that regulatory functions scale systematically across diverse systems: biological organisms (bacterial and eukaryotic genomes), human organizations (companies, federal agencies, universities), and decentralized entities (Wikipedia, cities). We combine an analysis of large data sets from each of these domains with a simple conceptual model. The model predicts that the scaling of regulatory costs shifts with system structure. Well-mixed small systems exhibit superlinear scaling between size and regulatory function, while modular large ones show sublinear or linear scaling, both in agreement with data. Finally, we find that socioeconomic systems that contain more diverse occupational functions tend to have more regulatory costs than expected from the scaling relationships, confirming the hypothesis that the type and complexity of interactions also play a role in regulatory costs. Our cross-system comparison offers a mechanistic framework for understanding regulatory function and can potentially guide efforts to analyze the costs and benefits of regulatory function in diverse systems.

nlin.AO↗

arXiv2025

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↗

arXiv2024

What makes Individual I's a Collective We; Coordination mechanisms & costs

The collective effort exceeds the sum of its parts when individuals coordinate and regulate their activities and behaviors. This holds true even in self-organizing systems with open, voluntary participation where coordination occurs implicitly. Here, we analyze the non-functional actions of contributors, administrators, and bots on Wikipedia, categorizing them by their asymmetric authority: one-way oversight and two-way. This categorization helps us reveal comparable patterns. First, we find remarkably consistent scaling factors for each category relative to system size. Two-way coordination scales superlinearly (with an exponent of $1.3$), while oversight coordination grows sublinearly (with an exponent of $0.9$), suggesting an underlying mechanism for coordination across communities. Second, we identify the hierarchical modular structure of interactions as a key factor for the economy of scale in coordination, and we propose a mathematical model to explain these results. Finally, our temporal analysis shows a shift from two-way interactions to one-way oversight as system size increases. This suggests the emergence of a nascent hierarchical structure even in self-organizing systems, echoing Weber's theory of organizational evolution.

physics.soc-ph↗

arXiv2023

Collective Intelligence as Infrastructure for Reducing Broad Global Catastrophic Risks

Academic and philanthropic communities have grown increasingly concerned with global catastrophic risks (GCRs), including artificial intelligence safety, pandemics, biosecurity, and nuclear war. Outcomes of many, if not all, risk situations hinge on the performance of human groups, such as whether governments or scientific communities can work effectively. We propose to think about these issues as Collective Intelligence (CI) problems -- of how to process distributed information effectively. CI is a transdisciplinary research area, whose application involves human and animal groups, markets, robotic swarms, collections of neurons, and other distributed systems. In this article, we argue that improving CI in human groups can improve general resilience against a wide variety of risks. We summarize findings from the CI literature on conditions that improve human group performance, and discuss ways existing CI findings may be applied to GCR mitigation. We also suggest several directions for future research at the exciting intersection of these two emerging fields.

nlin.AO↗

arXiv2022

Mathematical model bridges disparate timescales of lifelong learning

Lifelong learning occurs on timescales ranging from minutes to decades. People can lose themselves in a new skill, practicing for hours until exhausted. And they can pursue mastery over days or decades, perhaps abandoning old skills entirely to seek out new challenges. A full understanding of learning requires an account that integrates these timescales. Here, we present a minimal quantitative model that unifies the nested timescales of learning. Our dynamical model recovers classic accounts of skill acquisition, and describes how learning emerges from moment-to-moment dynamics of motivation, fatigue, and work, while also situated within longer-term dynamics of skill selection, mastery, and abandonment. We apply this model to explore the benefits and pitfalls of a variety of training regimes and to characterize individual differences in motivation and skill development. Our model connects previously disparate timescales -- and the subdisciplines that typically study each timescale in isolation -- to offer a unified account of the timecourse of skill acquisition.

physics.soc-ph↗

arXiv2021

Dynamical-System Model Predicts When Social Learners Impair Collective Performance

A key question concerning collective decisions is whether a social system can settle on the best available option when some members learn from others instead of evaluating the options on their own. This question is challenging to study, and previous research has reached mixed conclusions, because collective decision outcomes depend on the insufficiently understood complex system of cognitive strategies, task properties, and social influence processes. This study integrates these complex interactions together in one general yet partially analytically tractable mathematical framework using a dynamical system model. In particular, it investigates how the interplay of the proportion of social learners, the relative merit of options, and the type of conformity response affect collective decision outcomes in a binary choice. The model predicts that when the proportion of social learners exceeds a critical threshold, a bi-stable state appears in which the majority can end up favoring either the higher- or lower-merit option, depending on fluctuations and initial conditions. Below this threshold, the high-merit option is chosen by the majority. The critical threshold is determined by the conformity response function and the relative merits of the two options. The study helps reconcile disagreements about the effect of social learners on collective performance and proposes a mathematical framework that can be readily adapted to extensions investigating a wider variety of dynamics.

physics.soc-ph↗

arXiv2021

Falling Through the Cracks: Modeling the Formation of Social Category Boundaries

Social categorizations divide people into "us" and "them," often along continuous attributes such as political ideology or skin color. This division results in both positive consequences, such as a sense of community, and negative ones, such as group conflict. Further, individuals in the middle of the spectrum can fall through the cracks of this categorization process and are seen as out-group by individuals on either side of the spectrum, becoming inbetweeners. Here, we propose a quantitative, dynamical-system model that studies the joint influence of cognitive and social processes. We model where two social groups draw the boundaries between "us" and "them" on a continuous attribute. Our model predicts that both groups tend to draw a more restrictive boundary than the middle of the spectrum. As a result, each group sees the individuals in the middle of the attribute space as an out-group. We test this prediction using U.S. political survey data on how political independents are perceived by registered party members as well as existing experiments on the perception of racially ambiguous faces, and find support.

physics.soc-ph↗

arXiv2021

Scaling of Urban Income Inequality in the United States

Urban scaling analysis, the study of how aggregated urban features vary with the population of an urban area, provides a promising framework for discovering commonalities across cities and uncovering dynamics shared by cities across time and space. Here, we use the urban scaling framework to study an important, but under-explored feature in this community - income inequality. We propose a new method to study the scaling of income distributions by analyzing total income scaling in population percentiles. We show that income in the least wealthy decile (10%) scales close to linearly with city population, while income in the most wealthy decile scale with a significantly superlinear exponent. In contrast to the superlinear scaling of total income with city population, this decile scaling illustrates that the benefits of larger cities are increasingly unequally distributed. For the poorest income deciles, cities have no positive effect over the null expectation of a linear increase. We repeat our analysis after adjusting income by housing cost, and find similar results. We then further analyze the shapes of income distributions. First, we find that mean, variance, skewness, and kurtosis of income distributions all increase with city size. Second, the Kullback-Leibler divergence between a city's income distribution and that of the largest city decreases with city population, suggesting the overall shape of income distribution shifts with city population. As most urban scaling theories consider densifying interactions within cities as the fundamental process leading to the superlinear increase of many features, our results suggest this effect is only seen in the upper deciles of the cities. Our finding encourages future work to consider heterogeneous models of interactions to form a more coherent understanding of urban scaling.

physics.soc-ph↗

arXiv2020

Greetings from a Triparental Planet

In this work of speculative science, scientists from a distant star system explain the emergence and consequences of triparentalism, when three individuals are required for sexual reproduction, which is the standard form of mating on their home world. The report details the evolution of their reproductive system--that is, the conditions under which triparentalism and three self-avoiding mating types emerged as advantageous strategies for sexual reproduction. It also provides an overview of the biological consequences of triparental reproduction with three mating types, including the genetic mechanisms of triparental reproduction, asymmetries between the three mating types, and infection dynamics arising from their different mode of sexual reproduction. The report finishes by discussing how central aspects of their society, such as short-lasting unions among individuals and the rise of a monoculture, might have arisen as a result of their triparental system.

q-bio.PE↗

arXiv2020

Why are U.S. Parties So Polarized? A "Satisficing" Dynamical Model

Since the 1960s, Democrats and Republicans in U.S. Congress have taken increasingly polarized positions, while the public's policy positions have remained centrist and moderate. We explain this apparent contradiction by developing a dynamical model that predicts ideological positions of political parties. Our approach tackles the challenge of incorporating bounded rationality into mathematical models and integrates the empirical finding of satisficing decision making---voters settle for candidates who are "good enough" when deciding for whom to vote. We test the model using data from the U.S. Congress over the past 150 years, and find that our predictions are consistent with the two major political parties' historical trajectory. In particular, the model explains how polarization between the Democrats and Republicans since the 1960s could be a consequence of increasing ideological homogeneity within the parties.

physics.soc-ph↗

arXiv2019

Modeling the origin of urban output scaling laws

Urban outputs often scale superlinearly with city population. A difficulty in understanding the mechanism of this phenomenon is that different outputs differ considerably in their scaling behaviors. Here, we formulate a physics-based model for the origin of superlinear scaling in urban outputs by treating human interaction as a random process. Our model suggests that the increased likelihood of finding required collaborations in a larger population can explain this superlinear scaling, which our model predicts to be non-power-law. Moreover, the extent of superlinearity should be greater for activities that require more collaborators. We test this model using a novel dataset for seven crime types and find strong support.

physics.soc-ph↗

arXiv2018

Do two parties represent the US? Clustering analysis of US public ideology survey

Recent surveys have shown that an increasing portion of the US public believes the two major US parties adequately represent the US public opinion and think additional parties are needed. However, there are high barriers for third parties in political elections. In this paper, we aim to address two questions: "How well do the two major US parties represent the public's ideology?" and "Does a more-than-two-party system better represent the ideology of the public?". To address these questions, we utilize the American National Election Studies Time series dataset. We perform unsupervised clustering with Gaussian Mixture Model method on this dataset. When clustered into two clusters, we find a large centrist cluster and a small right-wing cluster. The Democratic Party's position (estimated using the mean position of the individuals self-identified with the parties) is similar to that of the centrist cluster, and the Republican Party's position is between the two clusters. We investigate if more than two parties represent the population better by comparing the Akaike Information Criteria for clustering results of the various number of clusters. We find that additional clusters give a better representation of the data, even after penalizing for the additional parameters. This suggests a multiparty system represents of the ideology of the public better.

cs.OH↗

arXiv2016

Prediction and Optimal Scheduling of Advertisements in Linear Television

Advertising is a crucial component of marketing and an important way for companies to raise awareness of goods and services in the marketplace. Advertising campaigns are designed to convey a marketing image or message to an audience of potential consumers and television commercials can be an effective way of transmitting these messages to a large audience. In order to meet the requirements for a typical advertising order, television content providers must provide advertisers with a predetermined number of "impressions" in the target demographic. However, because the number of impressions for a given program is not known a priori and because there are a limited number of time slots available for commercials, scheduling advertisements efficiently can be a challenging computational problem. In this case study, we compare a variety of methods for estimating future viewership patterns in a target demographic from past data. We also present a method for using those predictions to generate an optimal advertising schedule that satisfies campaign requirements while maximizing advertising revenue.

math.OC↗