Bulletin of the American Physical Society
APS March Meeting 2018
Monday–Friday, March 5–9, 2018; Los Angeles, California
Session E47: Statistical Mechanics of Social Systems |
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Sponsoring Units: GSNP Chair: Hans Hermann, ETH Zurich Room: LACC 507 |
Tuesday, March 6, 2018 8:00AM - 8:12AM |
E47.00001: Why Impact Factor Rankings Favor Small Journals Manolis Antonoyiannakis Can a comparison of two population averages be misleading? We show that, because of the natural differences within a population, an unweighted average metric tends to favor smaller entities over larger ones. When the degree of inequality within a population is small, this scale-dependence effect is of little practical importance. But for citations of scientific papers in journals, which can span up to four orders of magnitude, the effect is strong enough to interfere with impact factor rankings. By analyzing 166,498 journals in the 1997–2016 Journal Citation Reports of Clarivate Analytics (formerly Thomson Reuters), we have identified a boundary curve for the Impact Factor as a function of journal size. We confirm the functional form of this boundary curve by analyzing the top-cited portion of 2,700,000 papers published in 2015, and the citation distributions of several journals. We propose a scale-independent average citation index, as a potential remedy against the problem. |
Tuesday, March 6, 2018 8:12AM - 8:24AM |
E47.00002: Can Royalties Payments in the Mining Sector Be Taken as Options Lamine Dieng In the case of the Republic of Guinea, royalties payments are expressed in terms of the grade of Alumina, a taxation coefficient on each metric ton of bauxite and the three-month price of Aluminum at the London Metal Exchange (LME). In this work, we have examined the royalty's formula and found it to be identical to the options formula used in financial engineering. Options are financial derivatives used to protect investors against risky fluctuations of stocks and they are issued only to stock holders. Options were priced for the first time by using the Black-Scholes stochastic differential equation. Because of the mathematical similarity of the royalty's formula with the options formula priced by the Black-Scholes stochastic differential equation, we opted to replacing the price of Aluminum at the London Metal Exchange with the call options pricing formula. Now, instead of having the price of Aluminum as an index price, we had the price of Aluminum to be governed by the Black-Scholes stochastic differential equation which will make revenues from royalties to fluctuate as the price of Aluminum fluctuates. |
Tuesday, March 6, 2018 8:24AM - 8:36AM |
E47.00003: Dynamics of consensus formation on multiplex networks: The majority-vote model Jeehye Choi, Kwang-Il Goh Majority-vote model is a much-studied model for social opinion dynamics of two competing opinions. With the recent appreciation that our social network comprises a variety of different "layers" forming a multiplex network, a natural question arises on how such multiplex interactions affect the social opinion dynamics and consensus formation. Here, the majority-vote model will be studied on multiplex networks to understand the effect of multiplexity on opinion dynamics. We will discuss how global consensus is reached by three different types of voters-the normal, AND- and OR-rule voters--and what different microscopic origins are working in these different types. The AND-model reaches the largest consensus below Q_{c}. However, it needs much longer time to reach consensus than other models and the consensus collapses abruptly in the vicinity of the critical point. The OR-model has smaller level of consensus than AND-rule but it reaches the consensus more quickly thanks to weak endurances. The OR-model exhibits more active dynamics with more opinion flips as well as more disagreements than the AND-model, which render its consensus transition continuous at the critical point. The numerical simulation results are supported by analytical calculations based on approximate master equation. |
Tuesday, March 6, 2018 8:36AM - 8:48AM |
E47.00004: A bistable belief dynamics model for radicalization within sectarian conflict Yao-Li Chuang, Thomas Chou, Maria D'Orsogna Motivated by recent events, we propose a dynamical two-variable model to describe polarization, radicalization, and sectarian conflict. Individuals are described by a continuous belief variable and a discrete radicalization level indicating their tolerance to neighbors with different beliefs. A novelty of the model is the incorporation of a bistable radicalization process to address memory-dependent human social behavior. We show that bistability in behavior may be the reason for contradicting observations regarding whether social segregation exacerbates or alleviates conflicts. By extending our model to include a mechanism of institutional influence, such as propaganda or education, we show that the effectiveness of such intervention can be profoundly affected by the uncertainty due to bistability. In some parameter regimes, institutional influence may stifle the progression of radicalization, allowing a mixed population to achieve social conformity over time. In other cases, institutional influence adversely accelerates the spread of radicalization within a mixed population, implying that social segregation may be considered as a viable option against sectarian conflicts. |
Tuesday, March 6, 2018 8:48AM - 9:00AM |
E47.00005: Multiple complex emergent phenomena in a noise-driven statistical mechanical model of social dynamics Sean Fleming, Sara Del Valle We introduce an agent-based model of bilateral conflict and cooperation, unique in its construction and ability to generate three complex nonlinear emergent behaviors. It considers a conceptual index of cooperativity and consists of two partially coupled lattices containing three agent types: individualists, networkers, and reciprocators. The emergent phenomena are as follows. (1) A threshold process arises: if reciprocators out-number individualists by a certain margin, society-wide cooperativity is trapped in its initial condition; otherwise, it proceeds to a quasi-dynamical equilibrium. (2) Some asymmetric parameterizations between the populations produce aperiodic oscillations and a scale-free power spectrum in the time series of system-wide mean cooperativity with frequent small fluctuations and rare large events. This is a dynamical fractal akin to but genetically very different from that generated by Bak-Tang-Wiesenfeld or other cellular automaton models. (3) Omitting reciprocators gives a power-law degree distribution in the complex network formed by inter-agent correlations, with many poorly connected agents and a few super-nodes. This is a scale-free network akin to but genetically very different from that generated by, for example, the preferential attachment model. |
Tuesday, March 6, 2018 9:00AM - 9:12AM |
E47.00006: Evolution of Information in Prediction Markets Avaneesh Narla, Benjamin Machta Predictions can be interpreted as probability distributions for future events, updated as information becomes available. We consider political prediction data from PredictIt, a rich data set where millions of dollars are wagered on binary outcomes of elections and other world news. We quantify the gain of information in a time window as the Kullback-Leibler divergence between the market price at the start and end of the window. If world events actually occur according to their predictit prices, then the average of the gain of information should be equal to the average of the decrease in entropy in the same window. As a corollary, the sum of cumulative gains and entropy is expected to be constant as time evolves. These ensemble equalities allow us to make nontrivial tests of the hypothesis that PredictIt market prices are proper probability distributions. They hold within our error bars for the several hundred highest volume events analyzed. In this nontrivial way, trading market prices seem to act as probability distributions. We expect our equivalence can help characterize their temporal evolution. |
Tuesday, March 6, 2018 9:12AM - 9:24AM |
E47.00007: Surges of Collective Human Activity Emerge from Simple Pairwise Interactions Christopher Lynn, Evangelia Papadopoulos, Daniel Lee, Danielle Bassett Collective human behavior drives a wide range of social, political, and technological phenomena in the modern world. However, while the correlated activity of one or two individuals is partially understood, it remains unclear if and how these simple low-order interactions give rise to the complex large-scale patterns characteristic of human experience. Here we show that a network of email correspondence exhibits surges of collective activity, which cannot be explained by assuming humans act independently. Intuitively, this collective behavior could arise from complicated correlations between large groups of users, or from shared daily and weekly rhythms. Instead, we find that the network is quantitatively described by a maximum entropy model that depends only on simple pairwise interactions, making it equivalent to the Ising model. Remarkably, we find that the learned Ising interactions, which are inferred exclusively from the timing of sent emails, are closely related to the ground-truth topology of email correspondence. Together, these results suggest that patterns of collective activity emerge from simple pairwise correlations, which, in turn, are largely driven by direct inter-human communication. |
Tuesday, March 6, 2018 9:24AM - 9:36AM |
E47.00008: Correlated Opinion Dynamics in a Model of Collective Learning with Expert Advice Thiparat Chotibut, Tushar Vaidya, Georgios Piliouras Formation of correlated opinions in a network of interacting agents is a ubiquitous social phenomenon. In a financial market, for instance, traders’ opinions of private information such as a stock price oftentimes tend to be correlated, despite the complexity of opinion exchange mechanisms. Here, we introduce a minimal model of opinion dynamics that naturally exhibits formation of correlated opinions. In this model, each agent (trader) learns an estimate of private information (a stock price) from an expert (broker) while also updating its opinion by taking a weighted average of other opinions. When an expert can at best provide only an estimate of private information, typified by the truth masked with Gaussian white noise, the opinion dynamics is described by Langevin’s dynamics driven by one-dimensional noise. In this case, a stationary Gaussian distribution centered around the truth is developed, and correlated opinions emerge naturally with the correlations encoded in the stationary distribution. In addition, when agents learn from the expert at different rates, the dynamics violates detailed balance. We study in detail the non-equilibrium steady state associated with the correlated opinion dynamics of 2 agents. The case of 3 and higher number of agents is also discussed. |
Tuesday, March 6, 2018 9:36AM - 9:48AM |
E47.00009: Anomalous scaling of stochastic processes and the Moses effect Kevin Bassler, Lijian Chen, Joseph McCauley, Gemunu Gunaratne The state of a stochastic process evolving over a time t is typically assumed to lie on a normal distribution whose width scales like t^{1/2}. However, processes where the probability distribution is not normal and the scaling exponent differs from 1/2 are known. In processes with stationary increments, where the stochastic process is time-independent, auto-correlations between increments and infinite variance of increments can cause anomalous scaling. These sources have been referred to as the Joseph effect the Noah effect, respectively. If the increments are non-stationary, then scaling of increments with t can also lead to anomalous scaling, a mechanism we refer to as the Moses effect. Scaling exponents quantifying the three effects are defined and related to the Hurst exponent that characterizes the overall scaling of the stochastic process. Methods of time series analysis that enable accurate independent measurement of each exponent are presented. Simple stochastic processes illustrate each effect. Analysis of Intraday Financial time series data reveals that its anomalous scaling is due only to the Moses effect and the lack of Joseph effect implies that the market is efficient. |
Tuesday, March 6, 2018 9:48AM - 10:00AM |
E47.00010: Multiplex networks dynamics Miron Kaufman, Hung Diep, Sanda Kaufman In a recently proposed model (H. T. Diep, M. Kaufman, S. Kaufman, Physica A, 469, 183-199, 2017), we described the dynamics of interacting groups of individuals, such as might take place around a public decision. In this model, individuals of a given group interact with each other (intra-network), and also with individuals in other groups (across the networks). In each group, each individual has a preference, conceptualized as a (spin-like) variable s. A pair of individuals i, j within a group contributes -s_{i}*s_{j} to the intra-group energy. The inter-group energy of an individual i is taken to be proportional to the product between the preference s_{i} and the mean value of the preferences of the other group’s members. The noise in this system is quantified by the temperature T. We discuss the dynamics of the equivalent-neighbor networks where everyone interacts with everyone else. We explore the effect of the network topology by means of Monte-Carlo simulations on networks with short-range interactions. We also examine the dynamic consequences of adding a third group to the initial two. |
Tuesday, March 6, 2018 10:00AM - 10:12AM |
E47.00011: CARP Model for Multi-Risk Dynamics Alaa Moussawi, Xiang Niu, Noemi Derzsy, Xin Lin, Gyorgy Korniss, Boleslaw Szymanski Epidemic modeling has been studied for nearly a century, with most work focused on compartmental models where a single disease spreads amongst a large number of carriers. However, risks threatening modern societies form an intricately interconnected network infecting one carrier. Surprisingly little is known about how risk materializations in distinct domains influence each other. We present an approach in which expert assessments of likelihoods and influence of risks underlie a quantitative model of global risk dynamics. Using maximum likelihood estimation, we find the optimal model parameters and demonstrate that the network model significantly outperforms others, uncovering the full value of the expert crowd-sourced data. We analyze model dynamics and study the model resilience, stability and asymptotic trajectory. Our findings elucidate the identity of risks most detrimental to system stability at various points in time. The model provides quantitative means for measuring the adverse effects of risk interdependencies and the materialization of risks in the network and is shown to have a similar mean field approximation to that of traditional epidemiological models such as SIR. |
Tuesday, March 6, 2018 10:12AM - 10:24AM |
E47.00012: Statistical Mechanics of Design Greg Van Anders, Andrei Klishin, David Singer The design of complex behaviors is a challenge that exists in systems from the molecular- to the macroscale. In this talk, we will demonstrate that statistical mechanics techniques that were developed recently to meet design challenges in colloidal nanomaterials can be leveraged to gain new understanding into analogous design challenges that arise in the design of systems that are larger by ten orders of magnitude. We will give examples of the kind of design understanding that statistical mechanics can provide, which we term "systems physics", and point to large classes of new problem spaces where these approaches can be applied. |
Tuesday, March 6, 2018 10:24AM - 10:36AM |
E47.00013: Design Pressure and Stress in Systems Physics Andrei Klishin, Colin Shields, David Singer, Greg Van Anders A key challenge of the complex design problems that permeate science and engineering is the need to balance design objectives for specific design elements or subsystems with global system objectives. Global system objectives give rise to competing design pressures, whose effects can be difficult to trace in subsystem design. Here, using examples from layout problems, we show that the systems-level application of statistical physics principles, which we term ``systems physics'', provides a detailed characterization of stress in subsystem design. We analyze instances of routing problems in naval architectures, and show that systems physics provides direct means of classifying architecture types, and quantifying trade-offs between subsystem and overall performance. Our approach generalizes straightforwardly to design problems in a wide range of other disciplines that require concrete understanding of the link between the pressure to meet overall design objectives and the outcomes for subsystems. |
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