Tarik Hadzibeganovic

Cross-linguistic neuroimaging and dyslexia: A critical view

Recent neuro-cognitive theories of dyslexia presume that all dyslexics have the same type of brain abnormality irrespective of the particular writing system their language uses. In this article, we indicate how this presumption is inconsistent with cross-linguistic investigations of reading and dyslexia. There are two main issues. First, the information-processing requirements of reading vary greatly across different orthographies. Second, it is known that even within a single orthography there are different subtypes of dyslexia. Consequentially, it cannot be the case, not even within a single orthography let alone across orthographies, that all dyslexics have the same type of brain abnormality. Neuro-cognitive theorizing about dyslexia cannot afford to ignore these issues.

Evolution of ethnocentrism on undirected and directed Barabási–Albert networks

Using Monte Carlo simulations, we study the evolution of contingent cooperation and ethnocentrism in the one-shot game. Interactions and reproduction among computational agents are simulated on undirected and directed Barabasi–Albert (BA) networks. We first replicate the Hammond–Axelrod model of in-group favoritism on a square lattice and then generalize this model on undirected and directed BA networks for both asexual and sexual reproduction cases. Our simulations demonstrate that irrespective of the mode of reproduction, the ethnocentric strategy becomes common even though cooperation is individually costly and mechanisms such as reciprocity or conformity are absent. Moreover, our results indicate that the spread of favoritism towards similar others highly depends on the network topology and the associated heterogeneity of the studied population.

Cooperation in the snowdrift game on directed small-world networks under self-questioning and noisy conditions

Abstract Cooperation in the evolutionary snowdrift game with a self-questioning updating mechanism is studied on annealed and quenched small-world networks with directed couplings. Around the payoff parameter value r = 0.5 , we find a size-invariant symmetrical cooperation effect. While generally suppressing cooperation for r > 0.5 payoffs, rewired networks facilitated cooperative behavior for r 0.5 . Fair amounts of noise were found to break the observed symmetry and further weaken cooperation at relatively large values of r. However, in the absence of noise, the self-questioning mechanism recovers symmetrical behavior and elevates altruism even under large-reward conditions. Our results suggest that an updating mechanism of this type is necessary to stabilize cooperation in a spatially structured environment which is otherwise detrimental to cooperative behavior, especially at high cost-to-benefit ratios. Additionally, we employ component and local stability analyses to better understand the nature of the manifested dynamics.

Epidemic spreading on hierarchical geographical networks with mobile agents

Abstract Hierarchical geographical traffic networks are critical for our understanding of scaling laws in human trajectories. Here, we investigate the susceptible-infected epidemic process evolving on hierarchical networks in which agents randomly walk along the edges and establish contacts in network nodes. We employ a metapopulation modeling framework that allows us to explore the contagion spread patterns in relation to multi-scale mobility behaviors. A series of computer simulations revealed that a shifted power-law-like negative relationship between the peak timing of epidemics τ 0 and population density, and a logarithmic positive relationship between τ 0 and the network size, can both be explained by the gradual enlargement of fluctuations in the spreading process. We employ a semi-analytical method to better understand the nature of these relationships and the role of pertinent demographic factors. Additionally, we provide a quantitative discussion of the efficiency of a border screening procedure in delaying epidemic outbreaks on hierarchical networks, yielding a rather limited feasibility of this mitigation strategy but also its non-trivial dependence on population density, infector detectability, and the diversity of the susceptible region. Our results suggest that the interplay between the human spatial dynamics, network topology, and demographic factors can have important consequences for the global spreading and control of infectious diseases. These findings provide novel insights into the combined effects of human mobility and the organization of geographical networks on spreading processes, with important implications for both epidemiological research and health policy.

Randomness in the evolution of cooperation

Tag-based ethnocentric cooperation is a highly robust behavior which can evolve and prevail under a wide variety of conditions. Recent studies have demonstrated, however, that ethnocentrism can temporarily be suppressed by other competing strategies, especially in its early evolutionary stages. In a series of computational experiments, conducted with an agent-based evolutionary model of tag-mediated cooperation, we addressed the question of whether a stochastically established and once dominant non-ethnocentric strategy such as indiscriminate altruism can stably persist and permanently outweigh ethnocentrism. Our model, simulated on various complex network topologies, employs simple haploid genetics and asexual reproduction of computational agents equipped with memory and heritable phenotypic traits. We find that in combination with an implemented memory mechanism and tags, random bias acting in favor of altruists can lead to their long-lasting victory over all other types of strategists. The difference in density between altruistic and ethnocentric cooperators increases with greater rewiring of the underlying network, but decreases with growing population size. These findings suggest that randomness plays an important role in promoting non-ethnocentric cooperation and contributes to our understanding of how other than adaptive mechanisms can initiate the design of novel behavioral phenotypes, thereby shaping surprisingly new evolutionary pathways.

Cooperation and strategy coexistence in a tag-based multi-agent system with contingent mobility

Understanding how to enhance cooperation and coordination in distributed, open, and dynamic multiagent systems has been a grand challenge across disciplines. Knowledge employed in such systems is often limited and heuristic in nature such that cooperation-promoting mechanisms based on trust or reputation become largely unreliable. Although recent studies within the context of tag-based systems reported the emergence of stable cooperation in such uncertain environments, they were limited exclusively to only static interaction structures. Consequently, it remains unknown whether and under what conditions tag-based interactions can promote cooperation in dynamic mobile systems. We herein combine the methods of game theory, evolutionary computing, and agent-based simulation to study the emergence of tag-mediated cooperation in a mobile network with resource diversity. In a series of extensive Monte Carlo simulations, we find that tag-based interactions can give rise to high levels of cooperation even in the presence of different types of contingent mobility. Our model reveals that agent migrations within the system and the invasion of new agents from the outside can have similar effects on the evolution of dominant strategies. Interestingly enough, we observe a previously unreported coexistence of conditional and unconditional strategies in our tag-based model with costly migrations. In contrast to earlier studies, we show that this mobility-driven strategy coexistence in our model is not affected by resource limitations or other game-specific factors. Our findings highlight a striking robustness of tag-based cooperation under different mobility regimes, with important consequences for the future design of cooperation-enforcing protocols in large-scale, decentralized, and self-organizing systems such as peer-to-peer or mobile ad-hoc networks.

Evolution of tag-mediated altruistic behavior in one-shot encounters on large-scale complex networks

Abstract An agent-based evolutionary model of tag-mediated altruism is studied on large-scale complex networks addressing multiplayer one-shot Prisoner’s Dilemma-like games with four competing strategies. Contrary to standard theoretical predictions, but in line with recent empirical findings, we observed that altruistic acts in non-repeated interactions can emerge as a natural consequence of recognition of heritable phenotypic traits such as visual tags, which enable the discrimination between potentially beneficial and unproductive encounters. Moreover, we identified topological regimes in which cooperation always prevails at short times, but where unconditional cooperators are favored over conditional tag-based helpers, even though the latter initially have a slight reproductive advantage. After very long times, we found that all four strategies appeared about equally often, meaning that only one quarter of agents refused cooperation egoistically. However, our study suggests that intra-tag generosity can quickly evolve to dominate over other strategies in spatially structured environments that are otherwise detrimental to cooperative behavior.

Benefits of memory for the evolution of tag-based cooperation in structured populations

We study the effects of working memory capacity and network rewiring probability on the evolution of cooperation in the standard and modified versions of an agent-based model of tag-mediated altruism. In our evolutionary model, computational agents populate a large complex network, engage into multiplayer Prisoner’s Dilemma-like interactions, and reproduce sexually. Agents carry discernible phenotypic traits subject to mutation, memorize their own experiences, and employ different strategies when interacting with different types of co-players. Choices made are selected from a pool of two conditional and two unconditional strategies, depending on the available memory contents and phenotypic similarity among interactors. For the dominating strategy in our standard model version, we found a strong dependence of cooperation on network structure and a weak one on memory, whereas in the modified version, the structural effect was weaker than that of memory. Most importantly, we found that the previously reported decline of cooperation in memory-based models, typically observed at a high memory capacity, is now prevented with the help of tags. This suggests that the evolutionary advantages of memory capacity limits may be far more complex than previously assumed. For much smaller systems, we observed a quasi-symmetric alternation of the two winning groups of strategists. This result provides an example of ingroup biased interactions that are characterized by bursts of intra-tag cooperation interspersed with periods of unconditional transient altruism. Such switches of strategies may represent a boosting mechanism necessary for the emergence and stability of global altruism in its early evolutionary stages.

Boundary effects in a three-state modified voter model for languages

The standard three-state voter model is extended by including the outside pressure favouring one of the three language choices and by adding some biased internal noise. The Monte Carlo simulations are motivated by states with the population divided into three groups of various affinities to each other. We show the crucial influence of the boundaries for moderate lattice sizes like 500×500. By removing the fixed boundary at one side, we demonstrate that this can lead to the victory of one single choice. Noise in contrast stabilizes the choices of all three populations. In addition, we compute the persistence probability, i.e., the number of sites who have never changed their opinion during the simulation, and we consider the case of “rigid-minded” decision makers.

Agent-based Computer Simulations of Language Choice Dynamics

We use agent‐based Monte Carlo simulations to address the problem of language choice dynamics in a tripartite community that is linguistically homogeneous but politically divided. We observe the process of nonlocal pattern formation that causes populations to self‐organize into stable antagonistic groups as a result of the local dynamics of attraction and influence between individual computational agents. Our findings uncover some of the unique properties of opinion formation in social groups when the process is affected by asymmetric noise distribution, unstable intergroup boundaries, and different migratory behaviors. Although we focus on one particular study, the proposed stochastic dynamic models can be easily generalized and applied to investigate the evolution of other complex and nonlinear features of human collective behavior.