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Cooperation and deception through stigmergic interactions in human groups
Abstract
Stigmergy is a generic coordination mechanism widely used by animal societies, in which traces left by individuals in a medium guide and stimulate their subsequent actions. In humans, new forms of stigmergic processes have emerged through the development of online services that extensively use the digital traces left by their users. Here, we combine interactive experiments with faithful data-based modeling to investigate how groups of individuals exploit a simple rating system and the resulting traces in an information search task in competitive or noncompetitive conditions. We find that stigmergic interactions can help groups to collectively find the cells with the highest values in a table of hidden numbers. We show that individuals can be classified into three behavioral profiles that differ in their degree of cooperation. Moreover, the competitive situation prompts individuals to give deceptive ratings and reinforces the weight of private information versus social information in their decisions.
Stigmergy is a generic coordination mechanism widely used by animal societies, in which traces left by individuals in a medium guide and stimulate their subsequent actions. In humans, new forms of stigmergic processes have emerged through the development of online services that extensively use the digital traces left by their users. Here we combine interactive experiments with faithful data-based modeling to investigate how groups of individuals exploit a simple rating system and the resulting traces in an information search task in competitive or non-competitive conditions. We find that stigmergic interactions can help groups to collectively find the cells with the highest values in a table of hidden numbers. We show that individuals can be classified into three behavioral profiles that differ in their degree of cooperation. Moreover, the competitive situation prompts individuals to give deceptive ratings and reinforces the weight of private information versus social information in their decisions.
Swarm intelligence studies self-organized collective behavior resulting from interactions between individuals, typically in animals and artificial agents. Some studies from cognitive science have also demonstrated self-organization mechanisms in humans, often in pairs. Further research into the topic of human swarm intelligence could provide a better understanding of new behaviors and larger human collectives. This requires studies with multiple human participants in controlled experiments in a wide variety of scenarios, where a rich scope of possible interactions can be isolated and captured. In this paper, we present HuGoS—‘Humans Go Swarming’—a multi-user virtual environment implemented using the Unity game development platform, as a comprehensive tool for experimentation in human swarm intelligence. We demonstrate the functionality of HuGoS with naïve participants in a browser-based implementation, in a coordination task involving collective decision-making, messaging and signaling, and stigmergy. By making HuGoS available as open-source software, we hope to facilitate further research in the field of human swarm intelligence.
Recommender systems are nowadays a pervasive part of our online user experience, where they either serve as information filters or provide us with suggestions for additionally relevant content. These systems thereby influence which information is easily accessible to us and thus affect our decision-making processes though the automated selection and ranking of the presented content. Automated recommendations can therefore be seen as digital nudges, because they determine different aspects of the choice architecture for users. In this work, we examine the relationship between digital nudging and recommender systems, topics that so far were mostly investigated in isolation. Through a systematic literature search, we first identified 87 nudging mechanisms, which we categorize in a novel taxonomy. A subsequent analysis then shows that only a small part of these nudging mechanisms was previously investigated in the context of recommender systems. This indicates that there is a huge potential to develop future recommender systems that leverage the power of digital nudging in order to influence the decision-making of users. In this work, we therefore outline potential ways of integrating nudging mechanisms into recommender systems.
The impact of the Internet across multiple aspects of modern society is clear. However, the influence that it may have on our brain structure and functioning remains a central topic of investigation. Here we draw on recent psychological, psychiatric and neuroimaging findings to examine several key hypotheses on how the Internet may be changing our cognition. Specifically, we explore how unique features of the online world may be influencing: a) attentional capacities, as the constantly evolving stream of online information encourages our divided attention across multiple media sources, at the expense of sustained concentration; b) memory processes, as this vast and ubiquitous source of online information begins to shift the way we retrieve, store, and even value knowledge; and c) social cognition, as the ability for online social settings to resemble and evoke real‐world social processes creates a new interplay between the Internet and our social lives, including our self‐concepts and self‐esteem. Overall, the available evidence indicates that the Internet can produce both acute and sustained alterations in each of these areas of cognition, which may be reflected in changes in the brain. However, an emerging priority for future research is to determine the effects of extensive online media usage on cognitive development in youth, and examine how this may differ from cognitive outcomes and brain impact of uses of Internet in the elderly. We conclude by proposing how Internet research could be integrated into broader research settings to study how this unprecedented new facet of society can affect our cognition and the brain across the life course.
Significance
Digital technologies deeply impact the way that people interact. Therefore, it is crucial to understand how social influence affects individual and collective decision-making. We performed experiments where subjects had to answer questions and then revise their opinion after knowing the average opinion of some previous participants. Moreover, unbeknownst to the subjects, we added a controlled number of virtual participants always giving the true answer, thus precisely controlling social information. Our experiments and data-driven model show how social influence can help a group of individuals collectively improve its performance and accuracy in estimation tasks depending on the quality and quantity of information provided. Our model also shows how giving slightly incorrect information could drive the group to a better performance.
The capacity to use information provided by others to guide behavior is a widespread phenomenon in animal societies. A standard paradigm to test if and/or how animals use and transfer social information is through social diffusion experiments, by which researchers observe how information spreads within a group, sometimes by seeding new behavior in the population. In this article, we review the context, methodology and products of such social diffusion experiments. Our major focus is the transmission of information from an individual (or group thereof) to another, and the factors that can enhance or, more interestingly, inhibit it. We therefore also discuss reasons why social transmission sometimes does not occur despite being expected to. We span a full range of mechanisms and processes, from the nature of social information itself and the cognitive abilities of various species, to the idea of social competency and the constraints imposed by the social networks in which animals are embedded. We ultimately aim at a broad reflection on practical and theoretical issues arising when studying how social information spreads within animal groups.
Significance
Social insects build some of the most complex nests found in the animal kingdom. Here, we use experiments and modeling to decipher the mechanisms involved in the coordination of nest building in the ant Lasius niger : we first characterize nest architecture and its growth with 3D imaging techniques; then, we test the building responses of individual ants to artificial stimuli. A model entirely based on experimental data confirms that the individual level interactions and building rules are sufficient to reproduce the nest growth dynamics and the spatial patterns observed for real ant nests. Our results show that a pheromone added by ants to the building material is a key factor that controls the growth and form of nest architecture.
The concept of stigmergy, a mechanism for the coordination of actions via the trace they leave in a medium, can explain self-organizing activities in a broad range of domains, including social insects, collaborative websites, and human institutions. The present paper attempts to bring some order to these diverse applications by classifying varieties of stigmergy according to general aspects: the number of agents involved, the relative persistence or transience of the trace, the use of sematectonic or marker-based traces, and the quantitative or qualitative characteristics of traces. The resulting cases are essentially continuous, as more complex cases can be understood as having evolved out of simpler ones. One application is cognition, which can be viewed as an interiorization of the individual stigmergy that helps an agent to perform a complex project by registering the state of the work in the trace, thus providing an external memory. Another application is the evolution of cooperation, in which agents learn to profit from the synergy produced by the spontaneous stigmergic coordination between their initially independent actions, thus bypassing the problem of “free riders” that exploit the cooperators’ efforts.
Throughout our evolutionary history, our cognitive systems have been altered by the advent of technological inventions such as primitive tools, spoken language, writing, and arithmetic systems. Thirty years ago, the Internet surfaced as the latest technological invention poised to deeply reshape human cognition. With its multifaceted affordances, the Internet environment has profoundly transformed our thoughts and behaviors. Growing up with Internet technologies, "Digital Natives" gravitate toward "shallow" information processing behaviors characterized by rapid attention shifting and reduced deliberations. They engage in increased multitasking behaviors that are linked to increased distractibility and poor executive control abilities. Digital natives also exhibit higher prevalence of Internet-related addictive behaviors that reflect altered reward-processing and self-control mechanisms. Recent neuroimaging investigations have suggested associations between these Internet-related cognitive impacts and structural changes in the brain. Against mounting apprehension over the Internet's consequences on our cognitive systems, several researchers have lamented that these concerns were often exaggerated beyond existing scientific evidence. In the present review, we aim to provide an objective overview of the Internet's impacts on our cognitive systems. We critically discuss current empirical evidence about how the Internet environment has altered the cognitive behaviors and structures involved in information processing, executive control, and reward-processing.
© The Author(s) 2015.
The self-organisation of collective behaviours often manifests as dramatic patterns of emergent large-scale order. This is true for relatively "simple" entities such as microbial communities and robot "swarms," through to more complex self-organised systems such as those displayed by social insects, migrating herds, and many human activities. The principle of stigmergy describes those self-organised phenomena that emerge as a consequence of indirect communication between individuals of the group through the generation of persistent cues in the environment. Interestingly, despite numerous examples of multicellular behaviours of bacteria, the principle of stigmergy has yet to become an accepted theoretical framework that describes how bacterial collectives self-organise. Here we review some examples of multicellular bacterial behaviours in the context of stigmergy with the aim of bringing this powerful and elegant self-organisation principle to the attention of the microbial research community.
A proposed approach supports the continual construction and evolution of model-based Internetware artifacts by a collective of Internet-connected stakeholders. The key mechanism is incremental graph superimposition (IGS), a refinement of stigmergy, the process that produces collective intelligence in social insects. Employing IGS, a collective of individuals collaboratively and continually construct a collective-level graph by incrementally aggregating the individuals' working results. Each individual can work independently without direct interaction with others, facilitating mass collaboration among a large number of individuals. A conceptual model illustrates the effects of IGS. A Web environment supports IGS-based conceptual-model construction and ongoing case studies.
Bacterial biofilms are complex multicellular communities that are often associated with the emergence of large-scale patterns across the biofilm. How bacteria self-organize to form these structured communities is an area of active research. We have recently determined that the emergence of an intricate network of trails that forms during the twitching motility mediated expansion of Pseudomonas aeruginosa biofilms is attributed to an interconnected furrow system that is forged in the solidified nutrient media by aggregates of cells as they migrate across the media surface. This network acts as a means for self-organization of collective behavior during biofilm expansion as the cells following these vanguard aggregates were preferentially confined within the furrow network resulting in the formation of an intricate network of trails of cells. Here we further explore the process by which the intricate network of trails emerges. We have determined that the formation of the intricate network of furrows is associated with significant remodeling of the sub-stratum underlying the biofilm. The concept of stigmergy has been used to describe a variety of self-organization processes observed in higher organisms and abiotic systems that involve indirect communication via persistent cues in the environment left by individuals that influence the behavior of other individuals of the group at a later point in time. We propose that the concept of stigmergy can also be applied to describe self-organization of bacterial biofilms and can be included in the repertoire of systems used by bacteria to coordinate complex multicellular behaviors.
Social web content such as blogs, videos, and other user-generated content present a vast source of rich “digital-traces” of individuals' experiences. The use of digital traces to provide insight into human behavior remains underdeveloped. Recently, ontological approaches have been exploited for tagging and linking digital traces, with progress made in ontology models for well-defined domains. However, the process of conceptualization for ill-defined domains remains challenging, requiring interdisciplinary efforts to understand the main aspects and capture them in a computer processable form. The primary contribution of this article is a theory-driven approach to ontology development that supports semantic augmentation of digital traces. Specifically, we argue that (a) activity theory can be used to develop more insightful conceptual models of ill-defined activities, which (b) can be used to inform the development of an ontology, and (c) that this ontology can be used to guide the semantic augmentation of digital traces for making sense of phenomena. A case study of interpersonal communication is chosen to illustrate the applicability of the proposed multidisciplinary approach. The benefits of the approach are illustrated through an example application, demonstrating how it may be used to assemble and make sense of digital traces.
The editors introduce the themed issue “stigmergy 3.0”.
Significance
Marking animals avoid locations recently visited by others. We conceptualized this time nonlocal avoidance behavior as stigmergy, a form of mediated interaction that gives rise to coordinated behavior from seemingly independent individuals. In so doing, the concept of stigmergy is used beyond the realm of eusocial insects. To link the population spatiotemporal patterns that emerge from the individual nonlocal rules of interaction, we construct a collective movement model whereby randomly moving animals have the tendency to avoid marks left by a conspecific, depending on the age of the mark. As a result, we are able to quantify animal decision-making processes in terms of current and past locations of other individuals, linking behavior to history-dependent actions.
Because hedonic products consist predominantly of experience attributes, often with many available alternatives, choosing the “right” one is a demanding task for consumers. Decision making becomes even more difficult when a group, instead of an individual consumer, will consume the product, as is regularly the case for hedonic offerings such as movies, opera performances, and wine. Noting the prevalence of automated recommender systems as decision aids, the authors investigate the power of group recommender systems that consider the preferences of all group members. The authors develop a conceptual framework of the effects of group recommenders and empirically examine these effects through two choice experiments. They find that automated group recommenders offer more valuable information than single recommenders when the choice agent must consume the recommended alternative. However, when agents choose freely among alternatives, the group’s social relationship quality determines whether group recommenders actually create higher group value. Finally, group recommenders outperform decision making without automated recommendations if the agent’s intention to use the systems is high. A decision tree model of recommender usage offers guidance to hedonic product managers.
Joint memory is defined as the property of a social group in actualizing its own past transformations. It may result from the coupling of individual memories or be inscribed in the external environment. The concept of joint memory becomes heuristic when it subsumes an ensemble of social processes that are sufficiently integrated to be treated as an entity belonging to the real world. In social insects, colonies can perform action-perception loops which give rise to collective outcomes based on past experience; selective pressures could favour individual behavioural algorithms the combination of which provides a historical record organizing activities both in space and time. In contrast, social groups do not appear sufficiently autonomous in most vertebrates for joint memory be more than a way of seeing collective performances. However, in human beings, the development of external representations has made possible the growth of a socially distributed cognition; the accumulation of epigenetic events through cultural processes produces a set of material and symbolic products that feed back upon the conditions of evolution of the species.
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.
The roots of swarm intelligence are deeply embedded in the biological study of self-organized behaviors in social insects. From the routing of traffic in telecommunication networks to the design of control algorithms for groups of autonomous robots, the collective behaviors of these animals have inspired many of the foundational works in this emerging research field. For the first issue of this journal dedicated to swarm intelligence, we review the main biological principles that underlie the organization of insects' colonies. We begin with some reminders about the decentralized nature of such systems and we describe the un- derlying mechanisms of complex collective behaviors of social insects, from the concept of stigmergy to the theory of self-organization in biological systems. We emphasize in partic- ular the role of interactions and the importance of bifurcations that appear in the collective output of the colony when some of the system's parameters change. We then propose to categorize the collective behaviors displayed by insect colonies according to four functions that emerge at the level of the colony and that organize its global behavior. Finally, we ad- dress the role of modulations of individual behaviors by disturbances (either environmental or internal to the colony) in the overall flexibility of insect colonies. We conclude that fu- ture studies about self-organized biological behaviors should investigate such modulations to better understand how insect colonies adapt to uncertain worlds.
Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.
Stigmergy is a class of mechanisms that mediate animal-animal interactions. Its introduction in 1959 by Pierre-Paul Grassé made it possible to explain what had been until then considered paradoxical observations: In an insect society individuals work as if they were alone while their collective activities appear to be coordinated. In this article we describe the history of stigmergy in the context of social insects and discuss the general properties of two distinct stigmergic mechanisms: quantitative stigmergy and qualitative stigmergy.
In 4 experiments, the authors studied the influence of social motives on deception and strategic misrepresentation. In a newly developed information provision game, individuals faced a decision maker whose decision would affect both own and other's outcomes. By withholding information or by giving (in)accurate information about payoffs, participants could try to influence other's decision making. Less accurate and more inaccurate information was given when the decision maker was competitive rather than cooperative (Experiment 1), especially when participants had a prosocial rather than selfish value orientation (Experiments 3 and 4). Accurate information was withheld because of fear of exploitation and greed, and inaccurate information was given because of greed (Experiment 2). Finally, participants engaged in strategic misrepresentation that may trick competitive others into damaging their own and increasing the participant's outcomes.
In most studies of social learning in animals, no attempt has been made to examine the nature of the strategy adopted by animals when they copy others. Researchers have expended considerable effort in exploring the psychological processes that underlie social learning and amassed extensive data banks recording purported social learning in the field, but the contexts under which animals copy others remain unexplored. Yet, theoretical models used to investigate the adaptive advantages of social learning lead to the conclusion that social learning cannot be indiscriminate and that individuals should adopt strategies that dictate the circumstances under which they copy others and from whom they learn. In this article, I discuss a number of possible strategies that are predicted by theoretical analyses, including copy when uncertain, copy the majority, and copy if better, and consider the empirical evidence in support of each, drawing from both the animal and human social learning literature. Reliance on social learning strategies may be organized hierarchically, their being employed by animals when unlearned and asocially learned strategies prove ineffective but before animals take recourse in innovation.
By tapping into social cues, individuals in a group may gain access to higher-order computational capacities that mirror the group's responses to its environment.
. Collective intelligence is defined as the ability of a group to solve more problems than its individual members. It is argued that the obstacles created by individual cognitive limits and the difficulty of coordination can be overcome by using a collective mental map (CMM). A CMM is defined as an external memory with shared read/write access, that represents problem states, actions and preferences for actions. It can be formalized as a weighted, directed graph. The creation of a network of pheromone trails by ant colonies points us to some basic mechanisms of CMM development: averaging of individual preferences, amplification of weak links by positive feedback, and integration of specialised subnetworks through division of labor. Similar mechanisms can be used to transform the World-Wide Web into a CMM, by supplementing it with weighted links. Two types of algorithms are explored: 1) the co-occurrence of links in web pages or user selections can be used to compute a ma...
Cognition in animals is produced by the self-organized activity of mutually entrained body and brain. Given that stigmergy plays a major role in self-organization of societies, we identify stigmergic behavior in cognitive systems, as a common mechanism ranging from brain activity to social systems. We analyze natural societies and artificial systems exploiting stigmergy to produce cognition. Several authors have identified the importance of stigmergy in the behavior and cognition of social systems. However, the perspective of stigmergy playing a central role in brain activity is novel, to the best of our knowledge. We present several evidences of such processes in the brain and discuss their importance in the formation of cognition. With this we try to motivate further research on stigmergy as a relevant component for intelligent systems.
Online interaction is now a regular part of daily life for a demographically diverse population of hundreds of millions of people worldwide. These interactions generate fine-grained time-stamped records of human behavior and social interaction at the level of individual events, yet are global in scale, allowing researchers to address fundamental questions about social identity, status, conflict, cooperation, collective action, and diffusion, both by using observational data and by conducting in vivo field experiments. This unprecedented opportunity comes with a number of methodological challenges, including generalizing observations to the offline world, protecting individual privacy, and solving the logistical challenges posed by “big data” and web-based experiments. We review current advances in online social research and critically assess the theoretical and methodological opportunities and limitations.
[J]ust as the invention of the telescope revolutionized the study of the heavens, so too by rendering the unmeasurable measurable, the technological revolution in mobile, Web, and Internet communications has the potential to revolutionize our understanding of ourselves and how we interact…. [T]hree hundred years after Alexander Pope argued that the proper study of mankind should lie not in the heavens but in ourselves, we have finally found our telescope. Let the revolution begin. —Duncan Watts (2011 , p. 266)
By exploring the way in which certain animal groups coordinate among themselves,Collective Animal Behavioroffers a great deal of insight for managers seeking to better understand how collective behavior takes shape within a company. Using concrete examples, Sumpter . . . offers a clear account whose scope extends well beyond the natural sciences.
We study the evolution of cooperation in a population of agents playing the N-player Prisoner's Dilemma game via stigmergic interactions. Here, agent decision making is guided by a shared pheromone table. Actions are played at each time step and a trace (or signal) corresponding to the rewards received is recorded in this shared table. Subsequent actions are then determined probabilistically using the shared information. Comprehensive Monte Carlo simulation experiments show that the stigmergy-based mechanism is able to promote cooperation despite the fact that the make-up of the interacting groups is continually changing. A direct comparison with a genetic algorithm-based N-player model confirms that the extent of cooperative behaviour achieved is significantly higher across a wide range of cost-to-benefit ratios. In the concluding remarks, we highlight the real-world implications of stigmergic interactions.
This paper presents a model of trust and its interaction with information flow, influence, and control, and reports on an experiment based on the model to test several hypotheses about problem-solving effectiveness. The subjects were managers and the independent variable was the individual manager's initial level of trust. Groups of business executives were given identical factual information about a difficult manufacturing-marketing policy problem; half the groups were briefed to expect trusting behavior, the other half to expect untrusting behavior. There were highly significant differences in effectiveness between the high-trust groups and the low-trust groups in the clarification of goals, the reality of information exchanged, the scope of search for solutions, and the commitment of managers to implement solutions. The findings indicate that shared trust or lack of trust apparently are a significant determinant of managerial problem-solving effectiveness.
Online vendors employ collaborative filtering algorithms to provide recommendations to their customers so that they can increase their sales and profits. Although recommendation schemes are successful in e-commerce sites, they are vulnerable to shilling or profile injection attacks. On one hand, online shopping sites utilize collaborative filtering schemes to enhance their competitive edge over other companies. On the other hand, malicious users and/or competing vendors might decide to insert fake profiles into the user-item matrices in such a way so that they can affect the predicted ratings on behalf of their advantages. In the past decade, various studies have been conducted to scrutinize different shilling attacks strategies, profile injection attack types, shilling attack detection schemes, robust algorithms proposed to overcome such attacks, and evaluate them with respect to accuracy, cost/benefit, and overall performance. Due to their popularity and importance, we survey about shilling attacks in collaborative filtering algorithms. Giving an overall picture about various shilling attack types by introducing new classification attributes is imperative for further research. Explaining shilling attack detection schemes in detail and robust algorithms proposed so far might open a lead to develop new detection schemes and enhance such robust algorithms further, even propose new ones. Thus, we describe various attack types and introduce new dimensions for attack classification. Detailed description of the proposed detection and robust recommendation algorithms are given. Moreover, we briefly explain evaluation of the proposed schemes. We conclude the paper by discussing various open questions
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?”
Research on information sharing in group decision-making has widely assumed a cooperative context and focused on the exchange of shared or unshared information in the hidden profile paradigm ([Stasser and Titus, 1985] and [Stasser and Titus, 1987]), neglecting the role of information importance. We argue that information sharing is a mixed-motive conflict setting that gives rise to motivated strategic behavior. We introduce a research paradigm that combines aspects of the traditional information sampling paradigm with aspects of a public good dilemma: the information pooling game. In three experiments, we show that information sharing is strategic behavior that depends on people's pro-social or pro-self motivation, and that people consider information sharedness and information importance when deciding whether to reveal, withhold, or falsify their private or public information. Pro-social individuals were consistently found to honestly reveal their private and important information, while selfish individuals strategically concealed or even lied about their private and important information.
Stigmergy (the coordination of agents through signs they make and sense in a shared environment) was originally articulated in the study of social insects. Its basic processes are much simpler than those usually used to model human-level cognition. Thus it is an attractive way to coordinate agents in en- gineered environments such as robotics or information processing. Stigmergic coordination is not limited to insects. Humans regularly use environmentally- mediated signals to coordinate their activities. This paper develops a schema for analyzing stigmergy among humans, discusses examples (some using a computational environment and others antedating digital computation), and suggests how the use of such mechanisms may be extended.
We propose that intermediate forms of organization like joint-ventures are superior to markets and hierarchies when the costs of valuing complementary assets are non-trivial. By allowing piecemeal transactions under shared ownership and control, joint-ventures can reduce these costs significantly. Our theory is supported by the results of a cross-sectional analysis of the abnormal returns to the parent firms in 64 joint-venture announcements and 165 merger announcements.
A series of experiments shows how the andLasius niger uses its trail recruitment system to select between two food sources. Simultaneously presented with to 1M sucrose solution it concentrates on one of them. When offered a 1M solution together with a 0.1M solution it selects the richer source, unless the trait to the 0.1M source had become well-developed before the 1M source was introduced. In the same situation, however, the group/mass recruiting antTetramorium caespitum uses its more individual transmission of information to switch to the 1M source. A mathematical model describes these processes and its dynamics reflect the experimental results.Nous prsentons une srie d'expriences qui montrent comment la fourmiLasius niger peut utiliser son systme des recruitement par piste afin de slectionner une dfes deux sources de nourriture. Sil on leur offire simultanment deux solution 1M de saccharose, la sojcit'e concentre son activit sur l'une des deux. Si l'on offre deux solutions, une de 1M et l'autre de 0.1M, elle slectionne la plus riche, mons que la piste qui mne la source 0.1M soit dj bien dveloppe au moment o l'on introduit la source 1M. Face la mme situation, la fourmiTetramorium caespitum, qui recrute par groupe/masse, utilise son mode de transmission d'information plus, individuel pour changes son exploitation vers la source 1M. Un modle mathmatique dcrit ces processus, dont la dynamique correspond bien aux observations exprimentales.
The spontaneous organization of collective activities in animal groups and societies has attracted a considerable amount of attention over the last decade. This kind of coordination often permits group-living species to achieve collective tasks that are far beyond single individuals capabilities. In particular, a key benefit lies in the integration of partial knowledge of the environment at the collective level. In this contribution we discuss various self-organization phenomena in animal swarms and human crowds from the point of view of information exchange among individuals. In particular, we provide a general description of collective dynamics across species and introduce a classification of these dynamics not only with respect to the way information is transferred among individuals, but also with regard to the knowledge processing at the collective level. Finally, we highlight the fact that the individuals' ability to learn from past experiences can have a feedback effect on the collective dynamics, as experienced with the development of behavioral conventions in pedestrian crowds.
Psychologists, economists, and advertising moguls have long known that human decision-making is strongly influenced by the behavior of others. A rapidly accumulating body of evidence suggests that the same is true in animals. Individuals can use information arising from cues inadvertently produced by the behavior of other individuals with similar requirements. Many of these cues provide public information about the quality of alternatives. The use of public information is taxonomically widespread and can enhance fitness. Public information can lead to cultural evolution, which we suggest may then affect biological evolution.
Self-Organization in Biological Systems
- S Camazine
- Camazine S.
Stigmergy: A key driver of self-organization in bacterial biofilms
- E S Gloag
E. S. Gloag et al., Stigmergy: A key driver of self-organization in bacterial biofilms. Commun. Integr.
Biol. 6, e27331 (2013).
Cooperation and deception through stigmergic interactions in human groups
- T Bassanetti
T. Bassanetti et al., Cooperation and deception through stigmergic interactions in human groups.
Zenodo. https://doi.org/10.5281/zenodo.7924494. Deposited 11 May 2023.