An experimental study of the coloring problem on human subject networks

Adversarial Dynamics in Centralized Versus Decentralized Intelligent Systems

Artificial intelligence (AI) is often used to predict human behavior, thus potentially posing limitations to individuals' and collectives' freedom to act. AI's most controversial and contested applications range from targeted advertisements to crime prevention, including the suppression of civil disorder. Scholars and civil society watchdogs are discussing the oppressive dangers of AI being used by centralized institutions, like governments or private corporations. Some suggest that AI gives asymmetrical power to governments, compared to their citizens. On the other hand, civil protests often rely on distributed networks of activists without centralized leadership or planning. Civil protests create an adversarial tension between centralized and decentralized intelligence, opening the question of how distributed human networks can collectively adapt and outperform a hostile centralized AI trying to anticipate and control their activities. This paper leverages multi-agent reinforcement learning to simulate dynamics within a human-machine hybrid society. We ask how decentralized intelligent agents can collectively adapt when competing with a centralized predictive algorithm, wherein prediction involves suppressing coordination. In particular, we investigate an adversarial game between a collective of individual learners and a central predictive algorithm, each trained through deep Q-learning. We compare different predictive architectures and showcase conditions in which the adversarial nature of this dynamic pushes each intelligence to increase its behavioral complexity to outperform its counterpart. We further show that a shared predictive algorithm drives decentralized agents to align their behavior. This work sheds light on the totalitarian danger posed by AI and provides evidence that decentrally organized humans can overcome its risks by developing increasingly complex coordination strategies.

Humans strategically avoid connecting to others who agree and avert the emergence of network polarization in a coordination task

Clusters of like-minded individuals can impede consensus in group decision-making. We implemented an online color coordination task to investigate whether control over communication links creates clusters impeding group consensus. In 244 6-member networks, individuals were incentivized to reach a consensus by agreeing on a color, but had conflicting incentives for which color to choose. We varied (1) if communication links were static, changed randomly over time, or were player-controlled; (2) whether links determined who was observed or addressed; and (3) whether a majority existed or equally many individuals preferred each color. We found that individuals preferentially selected links to previously unobserved and disagreeing others, avoiding links with agreeing others. This prevented cluster formation, sped up consensus formation rather than impeding it, and increased the probability that the group agreed on the majority incentive. Overall, participants with a consensus goal avoided clusters by applying strategies that resolved uncertainty about others.

Link updating strategies influence consensus decisions as a function of the direction of communication

Consensus decision-making in social groups strongly depends on communication links that determine to whom individuals send, and from whom they receive, information. Here, we ask how consensus decisions are affected by strategic updating of links and how this effect varies with the direction of communication. We quantified the coevolution of link and opinion dynamics in a large population with binary opinions using mean-field numerical simulations of two voter-like models of opinion dynamics: an incoming model (IM) (where individuals choose who to receive opinions from) and an outgoing model (OM) (where individuals choose who to send opinions to). We show that individuals can bias group-level outcomes in their favour by breaking disagreeing links while receiving opinions (IM) and retaining disagreeing links while sending opinions (OM). Importantly, these biases can help the population avoid stalemates and achieve consensus. However, the role of disagreement avoidance is diluted in the presence of strong preferences-highly stubborn individuals can shape decisions to favour their preferences, giving rise to non-consensus outcomes. We conclude that collectively changing communication structures can bias consensus decisions, as a function of the strength of preferences and the direction of communication.

The role of position in consensus dynamics of polarizable networks

Communication constraints often complicate group decision-making. In this experiment, we investigate how the network position of opinionated group members determines both the speed and the outcome of group consensus in 7-member communication networks susceptible to polarization. To this end, we implemented an online version of a color coordination task within experimentally controlled communication networks. In 72 networks, one individual was incentivized to prefer one of two options. In 156 networks, two individuals were incentivized to prefer conflicting options. The network positions of incentivized individuals were varied. In networks with a single incentivized individual, network position played no significant role in either the speed or outcome of consensus decisions. For conflicts, the incentivized individual with more neighbors was more likely to sway the group to their preferred outcome. Furthermore, consensus emerged more slowly when the opponents had the same number of neighbors, but could not see each other's votes directly. These results suggest that the visibility of an opinion is key to wielding group influence, and that specific structures are sufficient to run communication networks into polarization, hindering a speedy consensus.

Partition Independent Set and Reduction-Based Approach for Partition Coloring Problem

Given a graph whose vertex set is partitioned, the partition coloring problem (PCP) requires the selection of one vertex from each partite set, such that the subgraph induced by the set of the selected vertices has the minimum chromatic number. Motivated by the routing and wavelength assignment problem for optical networks, PCP has been used to model many other real-world applications, such as dichotomy-based constraint encoding and scheduling problems. Solving PCP for large graphs is still a challenge since it is NP -complete. In this article, we first propose a key concept called a partition independent set (PIS) and design an efficient algorithm called FastPIS to find a maximum PIS. By applying FastPIS with a simple coloring procedure, we can obtain a high-quality initial solution for PCP. Moreover, we propose a reduction rule based on another novel concept called an l -clustering-degree bound ordered set ( l -CDBOS), by which the scale of the working graph can be iteratively reduced. Based on these techniques, we develop an efficient method called HotPGC for solving PCP. The proposed algorithm is evaluated on benchmark graphs, and computational results show that HotPGC achieves highly competitive performance, compared with the state-of-the-art algorithms. The influence of the proposed reduction rule on the efficiency of HotPGC is also analyzed.

Collective patterns and stable misunderstandings in networks striving for consensus without a common value system

Collective phenomena in systems of interacting agents have helped us understand diverse social, ecological and biological observations. The corresponding explanations are challenged by incorrect information processing. In particular, the models typically assume a shared understanding of signals or a common truth or value system, i.e., an agreement of whether the measurement or perception of information is ‘right’ or ‘wrong’. It is an open question whether a collective consensus can emerge without these conditions. Here we introduce a model of interacting agents that strive for consensus, however, each with only a subjective perception of the world. Our communication model does not presuppose a definition of right or wrong and the actors can hence not distinguish between correct and incorrect observations. Depending on a single parameter that governs how responsive the agents are to changing their world-view we observe a transition between an unordered phase of individuals that are not able to communicate with each other and a phase of an emerging shared signalling framework. We find that there are two types of convention-aligned clusters: one, where all social actors in the cluster have the same set of conventions, and one, where neighbouring actors have different but compatible conventions (‘stable misunderstandings’).

The dual problems of coordination and anti-coordination on random bipartite graphs

In some scenarios ("anti-coordination games"), individuals are better off choosing different actions than their neighbors while in other scenarios ("coordination games"), it is beneficial for individuals to choose the same strategy as their neighbors. Despite having different incentives and resulting population dynamics, it is largely unknown which collective outcome, anti-coordination or coordination, is easier to achieve. To address this issue, we focus on the distributed graph coloring problem on bipartite graphs. We show that with only two strategies, anti-coordination games (2-colorings) and coordination games (uniform colorings) are dual problems that are equally difficult to solve. To prove this, we construct an isomorphism between the Markov chains arising from the corresponding anti-coordination and coordination games under certain specific individual stochastic decision-making rules. Our results provide novel insights into solving collective action problems on networks.

Social Network Positions, Peer Effects, and Evaluation Updating: An Experimental Test in the Entrepreneurial Context

In many facets of life, individuals make evaluations that they may update after consulting with others in their networks. But not all individuals have the same positional opportunities for social interaction in a given network or the ability and desire to make use of those opportunities that are available to them. The configuration of a person’s network can also alter how information is spread or interpreted. To complicate matters further, scant research has considered how positions in social networks and the valence of network content interact because of the difficulty of (a) separating the “player” from the position in networks and (b) measuring all germane content in a particular network. This research develops a novel experimental platform that addresses these issues. Participants viewed and evaluated an entrepreneurial video pitch and were then randomly assigned to different networks, and positions within networks, and thus various opportunities for peer influence that were orthogonal to their network history, inclinations, attributes, or capabilities. Furthermore, all the content of social interaction, including its valence, was recorded to test underlying assumptions. Results reveal that those assigned to a position with brokerage opportunities in a network updated their evaluations of the entrepreneurial video considerably more negatively.

Self-organized division of cognitive labor

Often members of a group benefit from dividing the group’s task into separate components, where each member specializes their role so as to accomplish only one of the components. While this division of labor phenomenon has been observed with respect to both manual and cognitive labor, there is no clear understanding of the cognitive mechanisms allowing for its emergence, especially when there are multiple divisions possible and communication is limited. Indeed, maximization of expected utility often does not differentiate between alternative ways in which individuals could divide labor. We developed an iterative two-person game in which there are multiple ways of dividing labor, but in which it is not possible to explicitly negotiate a division. We implemented the game both as a human experimental task and as a computational model. Our results show that the majority of human dyads can finish the game with an efficient division of labor. Moreover, we fitted our computational model to the behavioral data, which allowed us to explain how the perceived similarity between a player’s actions and the task’s focal points guided the players’ choices from one round to the other, thus bridging the group dynamics and its underlying cognitive process. Potential applications of this model outside cognitive science include the improvement of cooperation in human groups, multi-agent systems, as well as human-robot collaboration.

Human-agent coordination in a group formation game

Coordination and cooperation between humans and autonomous agents in cooperative games raise interesting questions on human decision making and behaviour changes. Here we report our findings from a group formation game in a small-world network of different mixes of human and agent players, aiming to achieve connected clusters of the same colour by swapping places with neighbouring players using non-overlapping information. In the experiments the human players are incentivized by rewarding to prioritize their own cluster while the model of agents' decision making is derived from our previous experiment of purely cooperative game between human players. The experiments were performed by grouping the players in three different setups to investigate the overall effect of having cooperative autonomous agents within teams. We observe that the human subjects adjust to autonomous agents by being less risk averse, while keeping the overall performance efficient by splitting the behaviour into selfish and cooperative actions performed during the rounds of the game. Moreover, results from two hybrid human-agent setups suggest that the group composition affects the evolution of clusters. Our findings indicate that in purely or lesser cooperative settings, providing more control to humans could help in maximizing the overall performance of hybrid systems.

Random choices facilitate solutions to collective network coloring problems by artificial agents

Global coordination is required to solve a wide variety of challenging collective action problems from network colorings to the tragedy of the commons. Recent empirical study shows that the presence of a few noisy autonomous agents can greatly improve collective performance of humans in solving networked color coordination games. To provide analytical insights into the role of behavioral randomness, here we study myopic artificial agents attempting to solve similar network coloring problems using decision update rules that are only based on local information but allow random choices at various stages of their heuristic reasonings. We show that the resulting efficacy of resolving color conflicts is dependent on the implementation of random behavior of agents and specific population characteristics. Our work demonstrates that distributed greedy optimization algorithms exploiting local information should be deployed in combination with occasional exploration via random choices in order to overcome local minima and achieve global coordination.

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Local information makes solving distributed network coloring problems difficult

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Greedy agents can become gridlocked, making it difficult to find a global solution

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Agents making random choices can facilitate the finding of a global coloring

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Randomness can be finely tuned to a specific underlying population structure

Patterns of ties in problem-solving networks and their dynamic properties

Understanding the functions carried out by network subgraphs is important to revealing the organizing principles of diverse complex networks. Here, we study this question in the context of collaborative problem-solving, which is central to a variety of domains from engineering and medicine to economics and social planning. We analyze the frequency of all three- and four-node subgraphs in diverse real problem-solving networks. The results reveal a strong association between a dynamic property of network subgraphs-synchronizability-and the frequency and significance of these subgraphs in problem-solving networks. In particular, we show that highly-synchronizable subgraphs are overrepresented in the networks, while poorly-synchronizable subgraphs are underrepresented, suggesting that dynamical properties affect their prevalence, and thus the global structure of networks. We propose the possibility that selective pressures that favor more synchronizable subgraphs could account for their abundance in problem-solving networks. The empirical results also show that unrelated problem-solving networks display very similar local network structure, implying that network subgraphs could represent organizational routines that enable better coordination and control of problem-solving activities. The findings could also have potential implications in understanding the functionality of network subgraphs in other information-processing networks, including biological and social networks.

Synchronization of complex human networks

The synchronization of human networks is essential for our civilization and understanding its dynamics is important to many aspects of our lives. Human ensembles were investigated, but in noisy environments and with limited control over the network parameters which govern the network dynamics. Specifically, research has focused predominantly on all-to-all coupling, whereas current social networks and human interactions are often based on complex coupling configurations. Here, we study the synchronization between violin players in complex networks with full and accurate control over the network connectivity, coupling strength, and delay. We show that the players can tune their playing period and delete connections by ignoring frustrating signals, to find a stable solution. These additional degrees of freedom enable new strategies and yield better solutions than are possible within current models such as the Kuramoto model. Our results may influence numerous fields, including traffic management, epidemic control, and stock market dynamics.

Random Deviations Improve Micro-Macro Predictions: An Empirical Test

Many sociological theories make critically different macropredictions when their microassumptions are implemented stochastically rather than deterministically. Deviations from individuals' behavioral patterns described by microtheories can spark cascades that change macrooutcomes, even when deviations are rare and random. With two experiments, we empirically tested whether macrophenomena can be critically shaped by random deviations. Ninety-six percent of participants' decisions were in line with a deterministic theory of bounded rationality. Despite this impressive microlevel accuracy, the deterministic model failed to predict the observed macrooutcomes. However, a stochastic version of the same microtheory largely improved macropredictions. The stochastic model also correctly predicted the conditions under which deviations mattered. Results also supported the hypothesis that nonrandom deviations can result in fundamentally different macrooutcomes than random deviations. In conclusion, we echo the warning that deterministic microtheories can be misleading. Our findings show that taking into account deviations in sociological theories can improve explanations and predictions.

Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms

Attempting to imitate the brain's functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not directly advanced the field of machine learning (ML). Here, using neuronal cultures, we demonstrate that increased training frequency accelerates the neuronal adaptation processes. This mechanism was implemented on artificial neural networks, where a local learning step-size increases for coherent consecutive learning steps, and tested on a simple dataset of handwritten digits, MNIST. Based on our on-line learning results with a few handwriting examples, success rates for brain-inspired algorithms substantially outperform the commonly used ML algorithms. We speculate this emerging bridge from slow brain function to ML will promote ultrafast decision making under limited examples, which is the reality in many aspects of human activity, robotic control, and network optimization.

Group formation on a small-world: experiment and modelling

As a step towards studying human-agent collectives, we conduct an online game with human participants cooperating on a network. The game is presented in the context of achieving group formation through local coordination. The players set initially to a small-world network with limited information on the location of other players, coordinate their movements to arrange themselves into groups. To understand the decision-making process, we construct a data-driven model of agents based on probability matching. The model allows us to gather insight into the nature and degree of rationality employed by the human players. By varying the parameters in agent-based simulations, we are able to benchmark the human behaviour. We observe that while the players use the neighbourhood information in limited capacity, the perception of risk is optimal. We also find that for certain parameter ranges, the agents are able to act more efficiently when compared to the human players. This approach would allow us to simulate the collective dynamics in games with agents having varying strategies playing alongside human proxies.

Locally noisy autonomous agents improve global human coordination in network experiments

Coordination in groups faces a sub-optimization problem and theory suggests that some randomness may help to achieve global optima. Here we performed experiments involving a networked colour coordination game in which groups of humans interacted with autonomous software agents (known as bots). Subjects (n = 4,000) were embedded in networks (n = 230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locations. We show that bots acting with small levels of random noise and placed in central locations meaningfully improve the collective performance of human groups, accelerating the median solution time by 55.6%. This is especially the case when the coordination problem is hard. Behavioural randomness worked not only by making the task of humans to whom the bots were connected easier, but also by affecting the gameplay of the humans among themselves and hence creating further cascades of benefit in global coordination in these heterogeneous systems.

Brain and Social Networks: Fundamental Building Blocks of Human Experience

How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models.

Mandevillian intelligence

Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation (or even the accentuation) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success.

Can Simple Transmission Chains Foster Collective Intelligence in Binary-Choice Tasks?

In many social systems, groups of individuals can find remarkably efficient solutions to complex cognitive problems, sometimes even outperforming a single expert. The success of the group, however, crucially depends on how the judgments of the group members are aggregated to produce the collective answer. A large variety of such aggregation methods have been described in the literature, such as averaging the independent judgments, relying on the majority or setting up a group discussion. In the present work, we introduce a novel approach for aggregating judgments—the transmission chain—which has not yet been consistently evaluated in the context of collective intelligence. In a transmission chain, all group members have access to a unique collective solution and can improve it sequentially. Over repeated improvements, the collective solution that emerges reflects the judgments of every group members. We address the question of whether such a transmission chain can foster collective intelligence for binary-choice problems. In a series of numerical simulations, we explore the impact of various factors on the performance of the transmission chain, such as the group size, the model parameters, and the structure of the population. The performance of this method is compared to those of the majority rule and the confidence-weighted majority. Finally, we rely on two existing datasets of individuals performing a series of binary decisions to evaluate the expected performances of the three methods empirically. We find that the parameter space where the transmission chain has the best performance rarely appears in real datasets. We conclude that the transmission chain is best suited for other types of problems, such as those that have cumulative properties.

Formation of raiding parties for intergroup violence is mediated by social network structure

Intergroup violence is common among humans worldwide. To assess how within-group social dynamics contribute to risky, between-group conflict, we conducted a 3-y longitudinal study of the formation of raiding parties among the Nyangatom, a group of East African nomadic pastoralists currently engaged in small-scale warfare. We also mapped the social network structure of potential male raiders. Here, we show that the initiation of raids depends on the presence of specific leaders who tend to participate in many raids, to have more friends, and to occupy more central positions in the network. However, despite the different structural position of raid leaders, raid participants are recruited from the whole population, not just from the direct friends of leaders. An individual's decision to participate in a raid is strongly associated with the individual's social network position in relation to other participants. Moreover, nonleaders have a larger total impact on raid participation than leaders, despite leaders' greater connectivity. Thus, we find that leaders matter more for raid initiation than participant mobilization. Social networks may play a role in supporting risky collective action, amplify the emergence of raiding parties, and hence facilitate intergroup violence in small-scale societies.

An Experimental Study of Team Size and Performance on a Complex Task

The relationship between team size and productivity is a question of broad relevance across economics, psychology, and management science. For complex tasks, however, where both the potential benefits and costs of coordinated work increase with the number of workers, neither theoretical arguments nor empirical evidence consistently favor larger vs. smaller teams. Experimental findings, meanwhile, have relied on small groups and highly stylized tasks, hence are hard to generalize to realistic settings. Here we narrow the gap between real-world task complexity and experimental control, reporting results from an online experiment in which 47 teams of size ranging from n = 1 to 32 collaborated on a realistic crisis mapping task. We find that individuals in teams exerted lower overall effort than independent workers, in part by allocating their effort to less demanding (and less productive) sub-tasks; however, we also find that individuals in teams collaborated more with increasing team size. Directly comparing these competing effects, we find that the largest teams outperformed an equivalent number of independent workers, suggesting that gains to collaboration dominated losses to effort. Importantly, these teams also performed comparably to a field deployment of crisis mappers, suggesting that experiments of the type described here can help solve practical problems as well as advancing the science of collective intelligence.

Modeling crowdsourcing as collective problem solving

Crowdsourcing is a process of accumulating the ideas, thoughts or information from many independent participants, with aim to find the best solution for a given challenge. Modern information technologies allow for massive number of subjects to be involved in a more or less spontaneous way. Still, the full potentials of crowdsourcing are yet to be reached. We introduce a modeling framework through which we study the effectiveness of crowdsourcing in relation to the level of collectivism in facing the problem. Our findings reveal an intricate relationship between the number of participants and the difficulty of the problem, indicating the optimal size of the crowdsourced group. We discuss our results in the context of modern utilization of crowdsourcing.

From Neural and Social Cooperation to the Global Emergence of Cognition

The recent article (Turalska et al., 2012) discusses the emergence of intelligence via criticality as a consequence of locality breakdown. Herein, we use criticality for the foundation of a novel generation of game theory making the local interaction between players yield long-range effects. We first establish that criticality is not confined to the Ising-like structure of the sociological model of (Turalska et al., 2012), called the decision making model (DMM), through the study of the emergence of altruism using the altruism-selfishness model (ASM). Both models generate criticality, one by imitation of opinion (DMM) and the other by imitation of behavior (ASM). The dynamics of a sociological network 𝒮 influences the behavioral network ℱ through two game theoretic paradigms: (i) the value of altruism; (ii) the benefit of rapid consensus. In (i), the network 𝒮 debates the moral issue of altruism by means of the DMM, while at the level ℱ the individuals operate according to the ASM. The individuals of the level 𝒮, through a weak influence on the individuals of the level ℱ, exert a societal control on ℱ, fitting the principle of complexity management and complexity matching. In (ii), the benefit to society is the rapid attainment of consensus in the 𝒮 level. The agents of the level ℱ operate according to the prisoner's dilemma prescription, with the defectors acting as DMM contrarians at the level 𝒮. The contrarians, acting as the inhibitory links of neural networks, exert on society the same beneficial effect of maintaining the criticality-induced resilience that they generate in neural networks. The conflict between personal and social benefit makes the networks evolve toward criticality. Finally, we show that the theory of this article is compatible with recent discoveries in the burgeoning field of social neuroscience.

Collective behavior

The resurgence of interest in collective behavior is in large part due to tools recently made available for conducting laboratory experiments on groups, statistical methods for analyzing large data sets reflecting social interactions, the rapid growth of a diverse variety of online self-organized collectives, and computational modeling methods for understanding both universal and scenario-specific social patterns. We consider case studies of collective behavior along four attributes: the primary motivation of individuals within the group, kinds of interactions among individuals, typical dynamics that result from these interactions, and characteristic outcomes at the group level. With this framework, we compare the collective patterns of noninteracting decision makers, bee swarms, groups forming paths in physical and abstract spaces, sports teams, cooperation and competition for resource usage, and the spread and extension of innovations in an online community. Some critical issues surrounding collective behavior are then reviewed, including the questions of "Does group behavior always reduce to individual behavior?""Is 'group cognition' possible?" and "What is the value of formal modeling for understanding group behavior?"

Network Modularity is essential for evolution of cooperation under uncertainty

Cooperative behavior, which pervades nature, can be significantly enhanced when agents interact in a structured rather than random way; however, the key structural factors that affect cooperation are not well understood. Moreover, the role structure plays with cooperation has largely been studied through observing overall cooperation rather than the underlying components that together shape cooperative behavior. In this paper we address these two problems by first applying evolutionary games to a wide range of networks, where agents play the Prisoner's Dilemma with a three-component stochastic strategy, and then analyzing agent-based simulation results using principal component analysis. With these methods we study the evolution of trust, reciprocity and forgiveness as a function of several structural parameters. This work demonstrates that community structure, represented by network modularity, among all the tested structural parameters, has the most significant impact on the emergence of cooperative behavior, with forgiveness showing the largest sensitivity to community structure. We also show that increased community structure reduces the dispersion of trust and forgiveness, thereby reducing the network-level uncertainties for these two components; graph transitivity and degree also significantly influence the evolutionary dynamics of the population and the diversity of strategies at equilibrium.

Bidirectional selection between two classes in complex social networks

The bidirectional selection between two classes widely emerges in various social lives, such as commercial trading and mate choosing. Until now, the discussions on bidirectional selection in structured human society are quite limited. We demonstrated theoretically that the rate of successfully matching is affected greatly by individuals' neighborhoods in social networks, regardless of the type of networks. Furthermore, it is found that the high average degree of networks contributes to increasing rates of successful matches. The matching performance in different types of networks has been quantitatively investigated, revealing that the small-world networks reinforces the matching rate more than scale-free networks at given average degree. In addition, our analysis is consistent with the modeling result, which provides the theoretical understanding of underlying mechanisms of matching in complex networks.

Quality versus quantity of social ties in experimental cooperative networks

Recent studies suggest that allowing individuals to choose their partners can help to maintain cooperation in human social networks; this behaviour can supplement behavioural reciprocity, whereby humans are influenced to cooperate by peer pressure. However, it is unknown how the rate of forming and breaking social ties affects our capacity to cooperate. Here we use a series of online experiments involving 1,529 unique participants embedded in 90 experimental networks, to show that there is a ‘Goldilocks’ effect of network dynamism on cooperation. When the rate of change in social ties is too low, subjects choose to have many ties, even if they attach to defectors. When the rate is too high, cooperators cannot detach from defectors as much as defectors re-attach and, hence, subjects resort to behavioural reciprocity and switch their behaviour to defection. Optimal levels of cooperation are achieved at intermediate levels of change in social ties.

The effect of the rate of forming and breaking social ties on cooperative behaviour is not clear. Here the authors experimentally test the effect of rewiring the connections between individuals, and find that optimal levels of cooperation are achieved at intermediate levels of change in ties.

Measuring and modeling behavioral decision dynamics in collective evacuation

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In a controlled laboratory setting, our results quantify several key factors influencing individual evacuation decision making in a controlled laboratory setting. The experiment includes tensions between broadcast and peer-to-peer information, and contrasts the effects of temporal urgency associated with the imminence of the disaster and the effects of limited shelter capacity for evacuees. Based on empirical measurements of the cumulative rate of evacuations as a function of the instantaneous disaster likelihood, we develop a quantitative model for decision making that captures remarkably well the main features of observed collective behavior across many different scenarios. Moreover, this model captures the sensitivity of individual- and population-level decision behaviors to external pressures, and systematic deviations from the model provide meaningful estimates of variability in the collective response. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios, specifically the development and testing of robust strategies for training and control of evacuations that account for human behavior and network topologies.

Analytical reasoning task reveals limits of social learning in networks

Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

Processing power limits social group size: computational evidence for the cognitive costs of sociality

Sociality is primarily a coordination problem. However, the social (or communication) complexity hypothesis suggests that the kinds of information that can be acquired and processed may limit the size and/or complexity of social groups that a species can maintain. We use an agent-based model to test the hypothesis that the complexity of information processed influences the computational demands involved. We show that successive increases in the kinds of information processed allow organisms to break through the glass ceilings that otherwise limit the size of social groups: larger groups can only be achieved at the cost of more sophisticated kinds of information processing that are disadvantageous when optimal group size is small. These results simultaneously support both the social brain and the social complexity hypotheses.

Groups have a larger cognitive capacity than individuals

Increasing the number of options can paradoxically lead to worse decisions, a phenomenon known as cognitive overload [1]. This happens when an individual decision-maker attempts to digest information exceeding its processing capacity. Highly integrated groups, such as social insect colonies, make consensus decisions that combine the efforts of many members, suggesting that these groups can overcome individual limitations [2-4]. Here we report that an ant colony choosing a new nest site is less vulnerable to cognitive overload than an isolated ant making this decision on her own. We traced this improvement to differences in individual behavior. In whole colonies, each ant assesses only a small subset of available sites, and the colony combines their efforts to thoroughly explore all options. An isolated ant, on the other hand, must personally assess a larger number of sites to approach the same level of option coverage. By sharing the burden of assessment, the colony avoids overtaxing the abilities of its members.

A game-theoretic analysis of the ESP game

“Games with a Purpose” are interactive games that users play because they are fun, with the added benefit that the outcome of play is useful work. The ESP game, developed byy von Ahn and Dabbish [2004], is an example of such a game devised to label images on the web. Since labeling images is a hard problem for computer vision algorithms and can be tedious and time-consuming for humans, the ESP game provides humans with incentive to do useful work by being enjoyable to play. We present a simple game-theoretic model of the ESP game and characterize the equilibrium behavior in our model. Our equilibrium analysis supports the fact that users appear to coordinate on low effort words. We provide an alternate model of user preferences, modeling a change that could be induced through a different scoring method, and show that equilibrium behavior in this model coordinates on high-effort words. We also give sufficient conditions for coordinating on high-effort words to be a Bayesian-Nash equilibrium. Our results suggest the possibility of formal incentive design in achieving desirable system-wide outcomes for the purpose of human computation, complementing existing considerations of robustness against cheating and human factors.

Human matching behavior in social networks: an algorithmic perspective

We argue that algorithmic modeling is a powerful approach to understanding the collective dynamics of human behavior. We consider the task of pairing up individuals connected over a network, according to the following model: each individual is able to propose to match with and accept a proposal from a neighbor in the network; if a matched individual proposes to another neighbor or accepts another proposal, the current match will be broken; individuals can only observe whether their neighbors are currently matched but have no knowledge of the network topology or the status of other individuals; and all individuals have the common goal of maximizing the total number of matches. By examining the experimental data, we identify a behavioral principle called prudence, develop an algorithmic model, analyze its properties mathematically and by simulations, and validate the model with human subject experiments for various network sizes and topologies. Our results include i) a -approximate maximum matching is obtained in logarithmic time in the network size for bounded degree networks; ii) for any constant , a -approximate maximum matching is obtained in polynomial time, while obtaining a maximum matching can require an exponential time; and iii) convergence to a maximum matching is slower on preferential attachment networks than on small-world networks. These results allow us to predict that while humans can find a “good quality” matching quickly, they may be unable to find a maximum matching in feasible time. We show that the human subjects largely abide by prudence, and their collective behavior is closely tracked by the above predictions.

Structural diversity in social contagion

The concept of contagion has steadily expanded from its original grounding in epidemic disease to describe a vast array of processes that spread across networks, notably social phenomena such as fads, political opinions, the adoption of new technologies, and financial decisions. Traditional models of social contagion have been based on physical analogies with biological contagion, in which the probability that an individual is affected by the contagion grows monotonically with the size of his or her "contact neighborhood"--the number of affected individuals with whom he or she is in contact. Whereas this contact neighborhood hypothesis has formed the underpinning of essentially all current models, it has been challenging to evaluate it due to the difficulty in obtaining detailed data on individual network neighborhoods during the course of a large-scale contagion process. Here we study this question by analyzing the growth of Facebook, a rare example of a social process with genuinely global adoption. We find that the probability of contagion is tightly controlled by the number of connected components in an individual's contact neighborhood, rather than by the actual size of the neighborhood. Surprisingly, once this "structural diversity" is controlled for, the size of the contact neighborhood is in fact generally a negative predictor of contagion. More broadly, our analysis shows how data at the size and resolution of the Facebook network make possible the identification of subtle structural signals that go undetected at smaller scales yet hold pivotal predictive roles for the outcomes of social processes.