Complex Systems Research Exchange

Tuesday, 2024-03-12, 13:00 (KST/JST/UTC+9)

Seong-Gyu Yang (Korea Institute for Advanced Study)

Understanding biodiversity in large ecosystems with statistical physics approach

Numerous species coexist in nature, forming stable ecosystems despite competing for shared resources. The field of theoretical ecology has developed niche theory to elucidate the formation and sustenance of ecological systems. While the competitive exclusion principle (CEP) states that species with identical niches cannot coexist, observations in phytoplankton communities challenge this principle by showing diverse coexistence despite limited number of resources.

In this study, we introduce intraspecific suppression as a mechanism, extending a competition-based ecological model to comprehensively address coexistence and understand the high biodiversity. Through integration into the generalized MacArthur's consumer-resource model, we demonstrate how intraspecific suppression enhances biodiversity beyond CEP's predicted limit. Analyzing the relative diversity of coexisting consumers and resource kinds at steady state, we employ the cavity method and generating functional analysis to show analytically how this diversity can surpass unity, depending on the strength of intraspecific suppression. Supported by numerical simulations, we reveal that intraspecific suppression restricts the emergence of dominant species, fostering high biodiversity. Moreover, we explore how the effect of intraspecific suppression varies across different environmental conditions. This work offers a comprehensive framework within niche theory, incorporating intraspecific suppression to reconcile CEP's predictions with observed phenomena in ecological systems.

Read Seong-Gyu's work here.

Tuesday, 2024-01-30, 13:00 (KST/JST/UTC+9)

Minsuk Kim (Indiana University, Bloomington)

Shortest-path percolation on complex networks

In various infrastructural networks serving the transport of people or goods, path-based interactions play a significant role in sustaining their functionality. Thus, it is crucial to understand the robustness of such networks upon path-based perturbations. To tackle this problem, we propose a bond-percolation model describing the consumption and eventual exhaustion of resources of networks. In the model, a pair of origin-destination nodes is randomly selected at a time. If the shortest path distance between the selected pair of nodes is within the maximum budget, all edges along one of the randomly chosen shortest paths are removed from the network. As node pairs are selected, the initially connected network progressively fragments into disconnected clusters. We apply this model to Erdős–Rényi networks and fully characterize it by means of finite-size scaling analysis. With a finite maximal budget, the model displays a percolation transition identical to the one of the ordinary bond percolation. With an infinite budget, the transition is more abrupt than the one of the ordinary bond percolation yet smoother than the one that is displayed by the explosive percolation. 

Read Minsuk's work here.

Tuesday, 2023-12-19, 14:00 (KST/JST/UTC+9)

Erika Nozawa (Yamagata University)

Complex systems approach to phase inversion phenomena in food emulsions

Coupled map lattice (CML) is a powerful simulation approach that reproduces well complex and diverse patterns and motions in dynamical phenomena with spatial degrees of freedom. Indeed, CML has simulated various phenomena such as full range boiling, convection with turbulence transition, cloud formation predicting ‘guerrilla rainstorms’, and astronomical formation [1, 2] of transient grand-design spirals. 

We proposed a CML for simulating phase inversion processes from fresh cream to butter via whipped cream [3]. It is one of complex systems approaches to the pattern formation and self-organization of diverse food textures appearing in the phase inversion processes. The simulations exhibit two different phase inversion processes at high and low whipping temperatures (WTs). The overrun and viscosity changes in these processes are consistent with those in experiments. The two processes give rise to distinctive spatial patterns of overrun and viscosity, and are characterized on the viscosity-overrun plane which is one of the state diagrams, as the viscosity-dominant process at high WT and the overrun-dominant process at low WT, respectively. The butters obtained in the two processes were of low overrun and viscosity and of high overrun and viscosity, respectively in the butter region, and had, so to say, soft & creamy and hard & fluffy texture patterns. In the presentation, we will design a new texture (fluffy & creamy with moderate firmness) by controlling the cooking parameters of the CML procedures based on the above texture difference. We will also discuss our recent results on the size estimation of air bubbles and butter grains, important factors in texture design, if time permits.  

[1] E. Nozawa, "Coupled map lattice for the spiral pattern formation in astronomical objects", Physica D 405 (2020) 132377. (Preprint) 

[2] E. Nozawa, "Jammed Keplerian gas leads to the formation and disappearance of spiral arms in a coupled map lattice for astronomical objects", Progress of Theoretical and Experimental Physics 2023 (6) (2023) 063A02. 

[3] E. Nozawa and T. Deguchi, in preparation.

Tuesday, 2023-11-21, 13:00 (KST/JST/UTC+9)

Yohsuke Murase (RIKEN)

Indirect reciprocity beyond dichotomic reputation updates

Cooperation is a crucial aspect of social life, yet understanding the nature of cooperation and how it can be promoted is an ongoing challenge. One mechanism for cooperation is indirect reciprocity, with which individuals cooperate to maintain a good reputation. In models of indirect reciprocity, the interplay between an individual's actions and the resulting reputation is governed by a community's social norm. While many theoretical studies have been conducted to find norms that achieve stable cooperation, most previous studies conventionally assumed that reputations are binary values, either 'good' or 'bad.' Whereas this assumption has been widely adopted as a common practice for simplicity, such a simple dichotomy is not always realistic, and it is unclear whether the conclusions obtained for the binary-reputation models are valid for cases with more general and nuanced reputations.

This talk presents two studies addressing these limitations in understanding social norms and reputation systems. The first study [1] explores social norms with ternary reputations ('good', 'neutral', 'bad') using supercomputing, comprehensively analyzing cooperative norms beyond the well-known "leading eight" norms. The second study [2] extends the model by considering reputation updates for passive receivers and introducing stochastic elements. For this extended model, we theoretically obtained the necessary and sufficient conditions for cooperative Nash equilibria. Both studies reveal the common rules for successful norms and uncover norms with intriguing and counter-intuitive behaviors. These findings offer insights for designing effective norms in more realistic and nuanced reputation systems.

[1] Yohsuke Murase, Minjae Kim, Seung Ki Baek "Social norms in indirect reciprocity with ternary reputations" Scientific Reports, 12, 455 (2022)

[2] Yohsuke Murase, Christian Hilbe "Indirect reciprocity with stochastic and dual reputation updates" PLOS Computational Biology 19(7): e1011271 (2023)

Tuesday, 2023-10-31, 11:00 AM (KST/JST/UTC+9)

Jisung Yoon (Northwestern University)

Unsupervised embedding of trajectories captures the latent structure of scientific migration

Human migration and mobility drives major societal phenomena including epidemics, economies, innovation, and the diffusion of ideas. Although human mobility and migration have been heavily constrained by geographic distance throughout history, advances and globalization are making other factors such as language and culture increasingly more important. Advances in neural embedding models, originally designed for natural language, provide an opportunity to tame this complexity and open new avenues for the study of migration. Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation. The resulting representation provides a functional distance between locations, as well as a "digital double'' that can be distributed, re-used, and itself interrogated to understand the many dimensions of migration. We show that the unique power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility. Focusing on the case of scientific migration, we apply word2vec to a database of three million migration trajectories of scientists derived from the affiliations listed on their publication records. Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration, such as cultural, linguistic, and prestige relationships at multiple levels of granularity. Our results provide a theoretical foundation and methodological framework for using neural embeddings to represent and understand migration both within and beyond science.

Read Jisung's work here.