Ultrametricity increases the predictability of cultural dynamics

A quantitative understanding of societies requires useful combinations of empirical data and mathematical models. Models of cultural dynamics aim at explaining the emergence of culturally homogeneous groups through social influence. Traditionally, the initial cultural traits of individuals are chosen uniformly at random, the emphasis being on characterizing the model outcomes that are independent of these (`annealed') initial conditions. Here, motivated by an increasing interest in forecasting social behavior in the real world, we reverse the point of view and focus on the effect of specific (`quenched') initial conditions, including those obtained from real data, on the final cultural state. We study the predictability, rigorously defined in an information-theoretic sense, of the \emphsocial content of the final cultural groups (i.e. who ends up in which group) from the knowledge of the initial cultural traits. We find that, as compared to random and shuffled initial conditions, the hierarchical ultrametric-like organization of empirical cultural states significantly increases the predictability of the final social content by largely confining cultural convergence within the lower levels of the hierarchy. Moreover, predictability correlates with the compatibility of short-term social coordination and long-term cultural diversity, a property that has been recently found to be strong and robust in empirical data. We also introduce a null model generating initial conditions that retain the ultrametric representation of real data. Using this ultrametric model, predictability is highly enhanced with respect to the random and shuffled cases, confirming the usefulness of the empirical hierarchical organization of culture for forecasting the outcome of social influence models

Empirical signatures of universality, hierarchy and clustering in culture

In this thesis, "culture" refers to the collection of subjective human traits, such as preferences an opinions, that a given, geographically bounded population has at a given moment in time. Representative samples of individuals from such populations are studied, focusing on individual opinions expressed on various topics, present in multivariate empirical data that had been previously collected, mainly via social surveys. We propose and exploit new methods for analyzing such data, relying on mathematical notions specific to statistical mechanics and information theory, but also on agent-based models/simulations of opinion/cultural dynamics driven by social influence. These methods provide new insights about how human culture is organized. They provide indications that cultural structure has universal properties, independent of the geographical region and of the set of survey questions. Furthermore, these properties suggest that culture is shaped around a small number of "rationalities", while also having a certain hierarchical organization that is robust to social influence dynamics. Finally, we propose a method of filtering the noise in the data, which seems to allow for the identification of cultural modules that are not visible otherwise. However, we also show that visible modules may well be just artifacts of survey design. Theoretical Physic

A random matrix perspective of cultural structure: groups or redundancies?

Theoretical Physic

from the immune system to neural networks

Storing memory of molecular encounters is vital for an effective response to recurring external stimuli. Interestingly, memory strategies vary among different biological processes. These strategies range from networks that process input signals and retrieve an associative memory to specialized receptors that bind only to related stimuli. The adaptive immune system uses such a specialized strategy and can provide specific responses against many pathogens. During its response, the immune system retains some cells as memory to act quicker when reinfections with the same or evolved pathogens occur. However, differentiation of memory cells remains one of the least understood cell fate decisions in immunology. The ability of immune memory to recognize evolved pathogens makes it an ideal starting point to study learning and memory strategies for evolving environments—a topic with applications far beyond immunology. In this thesis, I present three projects that study different aspects of memory strategies for evolving stimuli. Indeed, we find that specialized memory strategies can follow the evolution of stimuli and reliably recover memory of previous encounters. In contrast, fully connected networks, such as Hopfield networks, fail to reliably recover the memory of evolving stimuli. Thus, pathogen evolution might be the reason that the immune system produces specialized memories. We further find that specialized memory receptors should trade off their maximal binding for cross-reactivity to bind to evolved targets. To produce such receptors, the differentiation into memory cells in the immune system should be highly regulated. Finally, we study update strategies of memory repertoires using an energy-based model. We find that repertoires should have a moderate risk tolerance to fluctuations in performance to adapt to the evolution of targets. Nevertheless, these systems can be very efficient in distinguishing between evolved versions of stored targets and novel random stimuli.2022-01-2

Coral community demographics: the variation between tropical and subtropical assemblages

Climate change is exposing coral reefs worldwide to increasingly recurrent disturbances. However, with current knowledge of coral population dynamics focused on long-term (i.e., asymptotic) characteristics, our capacity to forecast the resilience of coral communities, specifically, their resistance and recovery following disturbances, is restricted. Recurrent disturbances ensure that populations never achieve a stable equilibrium and will thus never attain their asymptotic trajectories. Instead, it is imperative that we quantify the performance of coral populations within non-stationary environments using their transient (i.e., short-term) dynamics, and evaluate the determinants of variation across these transient dynamics as conditions change. Here, I utilise state-structured demographic approaches and transient demographic theory to explore the association between abiotic variation and measures of demographic resilience. I illustrate how patterns in demographic resilience across animal and plant populations do not correlate with gradients in their exposure to abiotic variability, and thus recent experience of variable environments does not guarantee resilience to future climate variability. Next, I explore these insights in the context of resistance and recovery in coral populations to enhance understanding of coral community resilience. Using an Integral Projection Model framework, I show how, despite enduring more variable seasonal climates, subtropical coral communities remain vulnerable to future recurrent thermal stress. I also demonstrate how spatial variation in the transient dynamics of acroporid coral populations in southern Japan underpins the establishment of populations at higher latitudes. Finally, to further explore the mechanisms facilitating the establishment of subtropical coral populations, I evaluate spatial patterns in the impact of environmental variability on the long-term performance and transient dynamics of coral populations across coral taxa. Overall, this research represents a crucial step in quantifying the transient dynamics of coral populations, an approach which requires greater commitment if we are to anticipate the future resilience, viability, and condition of global coral communities

International asset pricing under partially integrated markets : measurement and models testing

Doctoral Program, Graduate School of Economics 平成25年3月19日授与甲第8号広島経済大