ICTS

Beginning with the intimate relationship between recursive algorithms and dynamical systems, I shall describe some common dynamics that serve as templates for `stateless' learning. This will be followed by reinforcement learning for dynamic systems, using Markov decision processes as a test case.

We will consider further applications in game theory and economics, such as bargaining problems, coalitional processes, bounded rationality, matching problems, housing markets. More detail will be provided on useful tricks and techniques used to prove these kinds of results.

Beginning with the intimate relationship between recursive algorithms and dynamical systems, I shall describe some common dynamics that serve as templates for `stateless' learning. This will be followed by reinforcement learning for dynamic systems, using Markov decision processes as a test case.

Cooperation is vital in both human and animal behavior, allowing individuals to achieve goals that would be difficult alone, such as hunting large, elusive prey. This cooperation has been integral to the evolution of conformity and group norms. However, it is unclear whether individuals conform primarily to acquire valuable information (informational conformity) or to blend in with the group (normative compliance), and under what conditions each form of conformity is exhibited. The degree of conformity may depend on factors like the nature of the activity, an individual’s expertise, and the reward structure. In activities such as foraging, where individuals often exhibit nearly optimal behaviors, one might expect informational conformity, as foragers likely know what is best for them. However, whether individuals conform in this way or are motivated by the desire to conform to group norms remains uncertain. This question forms the basis of our study. While patch foraging has been well studied in both wild and lab settings, most research has focused on individual foraging behavior, overlooking the role of group dynamics. In patchy environments, animals and humans typically behave in ways that align with the Marginal Value Theorem (MVT), but little attention has been given to how group foraging might influence individual behavior. Can suboptimal foragers influence others, leading to less optimal group outcomes? This study explores the social dynamics of group foraging through a novel task, examining whether collective behavior can lead individuals away from optimal foraging, indicating normative conformity. Additionally, our research aims to develop process-level models of learning and decision-making, enhancing our understanding of the mechanisms underlying conformity in group foraging.

In a series of four lectures, I give an introduction to evolutionary game theory and the literature on the evolution of cooperation. This series covers
(i) Evolutionary game theory in infinite and finite populations (Replicator dynamics, Moran process);
(ii) Evolution of cooperation and direct reciprocity
(iii) Social norms and the evolution of indirect reciprocity
(iv) Some current research directions (e.g., direct reciprocity in complex environments).

In a series of four lectures, I give an introduction to evolutionary game theory and the literature on the evolution of cooperation. This series covers
(i) Evolutionary game theory in infinite and finite populations (Replicator dynamics, Moran process);
(ii) Evolution of cooperation and direct reciprocity
(iii) Social norms and the evolution of indirect reciprocity
(iv) Some current research directions (e.g., direct reciprocity in complex environments).

Infectious diseases or epidemics spread through human society via social interactions among infected and healthy individuals. In this talk, we explore the coupled evolution of the epidemic and protection adoption behavior of humans.

In the first part of the talk, we focus on the class of susceptible-infected-susceptible (SIS) epidemic model where individuals choose whether to adopt protection or not based on the trade-off between the cost of adopting protection and the risk of infection; the latter depends on the current prevalence of the epidemic and the fraction of individuals who adopt protection in the entire population. We define the coupled epidemic-behavioral dynamics by modeling the evolution of individual protection adoption behavior according to the replicator dynamics. We fully characterize the equilibria and their stability properties. We further analyze the coupled dynamics under timescale separation when individual behavior evolves faster than the epidemic, and characterize the equilibria of the resulting discontinuous hybrid dynamical system. Numerical results illustrate how the coupled dynamics exhibits oscillatory behavior and convergence to sliding mode solutions under suitable parameter regimes.

In the second part of the talk, we discuss a dynamic population game model to capture individual behavior against susceptible-asymptomatic-infected-recovered (SAIR) epidemic model. Each node chooses whether to activate (i.e., interact with others), how many other individuals to interact with, and which zone to move to in a time-scale which is comparable with the epidemic evolution. We define and analyze the notion of equilibrium in this game, and investigate the transient behavior of the epidemic spread in a range of numerical case studies, providing insights on the effects of the agents' degree of future awareness, strategic migration decisions, as well as different levels of lockdown and other interventions.

In a series of four lectures, I give an introduction to evolutionary game theory and the literature on the evolution of cooperation. This series covers
(i) Evolutionary game theory in infinite and finite populations (Replicator dynamics, Moran process);
(ii) Evolution of cooperation and direct reciprocity
(iii) Social norms and the evolution of indirect reciprocity
(iv) Some current research directions (e.g., direct reciprocity in complex environments).

Animals in the natural world face many choices, and their decisions with respect to food and habitat have major consequences for their fitness. Many factors influence these behavioural decisions, including the ecological and life history context of individuals. I will present our work analysing how females of a cosmopolitan and generalist pest — the red flour beetle Tribolium castaneum — choose where and how to lay eggs. When presented with a choice of an optimal (wheat flour) vs. a non-optimal resource (finger millet), females sometimes allocate more eggs in finger millet. However, we find that this preference depends on their age and density context, and is tuned to optimize distinct fitness components for their offspring, likely mediated via differential nutrient provisioning. During laboratory evolution in wheat-finger millet habitats, the founder female context also determines evolutionary changes in decision-making, though these maternal effects decline over time. Importantly, founder context also influenced population size and the effect of an inadvertent parasitic infection in our experiment. Our work highlights the role of ecological context in driving female decision-making, and demonstrates some wide-ranging effects of founder context on adaptation and trait evolution.

Many theorists have employed game theory to model the emergence of stable social norms, or natural “social contracts.” One branch of this literature uses bargaining games to show why many societies have norms and rules for fairness. In cultural evolutionary models, fair bargaining emerges endogenously because it is an efficient way to divide resources. But these models miss an important element of real human societies – divisions into groups or social categories. Once such groups are added to cultural evolutionary models, fairness is no longer the expected outcome.  Instead “discriminatory norms” often emerge where one group systematically gets more when dividing resources. I show why the addition of categories to bargaining models leads to unfairness, and discuss the role of power us in this process. I also address how categories might emerge to support inequity, and the possibility of modeling social change. Altogether this work emphasizes that if one wishes to understand the naturalistic emergence of social contracts, one must account for the presence of categorical divisions, and unfairness, as well as for norms of fairness.

Multiplayer Evolutionary Games (MEGs) extend classical evolutionary game theory by incorporating interactions among multiple participants rather than just two. This lecture introduces MEGs and their foundational connection to population genetics and their evolutionary dynamics. Theoretical principles such as fitness, selection, and mutation are explored, illustrating how MEGs capture non-linear interaction effects. The importance of higher-order interactions is emphasized, demonstrating how MEGs naturally extend traditional evolutionary models to more complex, real-world scenarios.

Here we extend the Bala-Goyal framework to include differential levels of trust.  We will discuss how this modification might be used to model groups of scientists making judgments about the reliability of one another's work, and show how introducing trust dynamics can both slow learning and, in some cases, lead to stably polarized outcomes.  We will also discuss how agents learning over multiple domains can come to form epistemic factions, where unrelated beliefs become correlated.

This lecture examines the long-term behaviour of MEGs, focusing on fixation probabilities, fixation times, and stochastic slowdowns. It explores key questions such as if, when, and how a strategy persists in the long run. The transition from static equilibrium analysis to dynamic evolution is discussed, incorporating concepts like mutation-selection equilibrium, the 1/3 rule, risk dominance, and their generalisations to multiplayer settings. Additionally, the role of multiplayer games in mutualism is highlighted, showing how cooperative interactions persist over time in an ecological framework.

In evolutionary game theory, a relative comparison of the cost and benefit associated with obtaining a resource, called payoff, is used as an
indicator of fitness of an organism. Payoffs of different strategies, quantitatively represented as payoff matrices, are used to understand complex inter-species and intra-species interactions like cooperation, mutualism, and altruism. Payoff matrices, however, are usually treated as invariant with time —largely due to the absence of any empirical data quantifying their evolution. In this talk, I will present empirical evidence of three types of resource-dependent changes in the payoff matrices of evolving Saccharomyces cerevisiae populations. Our results show that depending on the carbon source and participating genotypes, N-player games could collapse, be born, or be maintained.

Expanding beyond traditional evolutionary games, this lecture discusses higher-order interactions in ecology and their connection to evolutionary dynamics. The mathematical connection between Lotka-Volterra dynamics and replicator equations is explored, illustrating how ecological and evolutionary processes interlink even in higher dimensions. Moving to social systems, we discuss the evolution of collective beliefs and trust, providing insights into the role of MEGs in shaping human social structures.

These lectures underscore the versatility of MEGs in explaining the complex nature of both natural selection and cultural evolution.

The 2021 WHO Malaria Report revealed that most Sub-Saharan African countries fell short of the 2020 Global Technical Strategy (GTS) targets, largely due to inconsistent use of insecticide-treated nets (ITNs) driven by various socioeconomic factors.

This research talk leverages data from 38 SSA countries and game-theoretic models to uncover key patterns and emerging trends in malaria control. By analyzing historical data and generating optimized projections, the study identifies critical barriers—including economic constraints, behavioural resistance, and declining ITN efficacy—that contributed to these shortfalls. It also provides country-specific recommendations on the likelihood of achieving the GTS 2025 and 2030 goals.

Simulated intervention scenarios highlight actionable strategies, particularly the role of public-sector engagement in subsidizing ITN distribution to mitigate financial barriers for vulnerable populations. Additionally, using the Cobb-Douglas production model, the study demonstrates how integrated strategies can enhance donor-funded efforts and promote long-term economic sustainability within malaria elimination programs.

This talk will emphasize the necessity of aligning public health interventions with economic policies to sustain high ITN coverage and accelerate progress toward malaria eradication.

The facultatively multicellular amoeba Dictyostelium discoideum is a model system for studying the transition from unicellular to multicellular organization. It is a remarkable example of microbes with aggregative life cycles, which evolved multiple times independently in distant taxa. A key feature of such cycles is that multicellular aggregates can contain cells that are genetically different, and are therefore subjected to a high degree of conflict.

I will discuss different solutions that have been proposed to account for the evolutionary persistence of such apparently paradoxical life cycles. In particular, in the light of recent experimental results, I will address the link between proximate causes, rooted in single-cell mechanics, and ultimate causation. I will also show how agent-based and eco-evolutionary models can be used to understand the role of heterogeneous motility in the evolution of aggregative life cycles.

Biological communities are endowed with properties, such as diversity, primary production, or total biomass, that have ecological relevance. The possibility that such properties are shaped by natural selection has been tested by experimentally selecting microbial communities based on some collective function.

I will discuss different models for artificial community selection and discuss in particular its effect on species traits, notably on the interaction parameters.

 In this era of omnipresent social media, social contagions are becoming a growing matter of interest. Our work integrates insights from systematic survey data, the tool of choice for social opinion exploration, with computational models to demonstrate a novel framework for deriving compartmental models from open-ended questions. By analyzing free-form survey responses and qualitative narratives, we systematically map individual opinions and behaviors into discrete compartments that mirror the stages of influence and adoption observed in various peer influenced dynamics, like public health and marketing campaigns. In the vaccine perception domain, respondents’ descriptions of peer interactions and protective behavior are classified into states analogous to susceptible, influenced, and resistant, capturing the dynamics of opinion formation and behavioral change. Similarly, in the referral marketing scenario, open-ended responses reveal latent engagement stages that inform a compartmental structure reflective of awareness, participation, and advocacy. Our quantitative treatment shows that these data-driven compartments can be effectively incorporated into dynamical systems models, giving rise to interesting opinion diffusion patterns. The proposed framework not only bridges qualitative insights with rigorous mathematical modeling but also highlights the broader applicability of compartmental approaches in deciphering complex social processes from open-ended inquiry.

Philosophers and economists have used cultural evolutionary models of bargaining to understand issues related to fairness and justice, and especially how fair and unfair conventions and norms might arise in human societies. One line of this research shows how the presence of social categories in such models allows for inequitable equilibria that are not possible in models without social categories. This is taken to help explain why in human groups with social categories inequity is often the rule rather than the exception. But in previous models, it is typically assumed that these categories are rigid, easily observable, and binary. In reality, social categories are not always so tidy. We introduce evolutionary models where the tags connected with social categories can be flexible, variable, or difficult to observe, i.e., where these tags can carry different amounts of information about group membership. We show how alterations to these tags can undermine the stability of unfair conventions. We argue that these results can inform projects intended to ameliorate inequity, especially projects that seek to alter the properties of category markers.