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Human swarming, a real-time method for parallel distributed intelligence
Abstract
Although substantial research has explored the design of artificial swarms, the majority of such work involves swarms of autonomous robots or simulated agents. Little work, however, has been done on the creation of artificial swarms that connect groups of networked humans with the objective of fostering a unified emergent intelligence. This paper describes a novel platform called UNU that enables distributed populations of networked users to congregate online in real-time swarms and tackle problems as an artificial swarm intelligence (A.S.I.). Modeled after biological swarms, the UNU platform enables online groups to work together in synchrony, forging a unified dynamic system that can quickly answer questions and make decisions by exploring a decision-space and converging on a preferred solution. Initial testing suggests that human swarming has great potential for unleashing the collective intelligence of online groups, often exceeding individual abilities.
... More specifically, swarming can occur among groups of online users by closing the loop around populations of networked individuals [51]. Swarm AI is a real-time online environment for human swarming developed by Unanimous A.I and modeled after the behavior of honey bees [50]. It is the computational glue that allows people to work together in real-time swarms that can integrate noisy evidence, weigh competing alternatives, and converge on final decisions. ...
... Participants who are confident in their answer will work harder to keep their magnets close, while others who aren't as confident may let their magnets drift away. All of this information is combined with Artificial Intelligence algorithms to drive the puck and make decisions [50]. ...
... People are smart, but groups of people are even smarter. There has been over 100 years of research dating back to 1906 where Francis Gaulden famously studied the Wisdom of the Crowd (WOC) [50]. He attended a county fair in London and asked hundreds of farmers to estimate the weight of an ox. ...
Modern companies are increasingly relying on groups of individuals to reach organizational goals and objectives, however many organizations struggle to cultivate optimal teams that can maximize performance. Fortunately, existing research has established that group personality composition (GPC), across five dimensions of personality, is a promising indicator of team effectiveness. Additionally, recent advances in technology have enabled groups of humans to form real-time, closed-loop systems that are modeled after natural swarms, like flocks of birds and colonies of bees. These Artificial Swarm Intelligences (ASI) have been shown to amplify performance in a wide range of tasks, from forecasting financial markets to prioritizing conflicting objectives. The present research examines the effects of group personality composition on team performance and investigates the impact of measuring GPC through ASI systems. 541 participants, across 111 groups, were administered a set of well-accepted and vetted psychometric assessments to capture the personality configurations and social sensitivities of teams. While group-level personality averages explained 10% of the variance in team performance, when group personality composition was measured through human swarms, it was able to explain 29% of the variance, representing a 19% amplification in predictive capacity. Finally, a series of machine learning models were applied and trained to predict group effectiveness. Multivariate Linear Regression and Logistic Regression achieved the highest performance exhibiting 0.19 mean squared error and 81.8% classification accuracy.
... [58] The role of individual self-efficacy in solving cooperative problems is studied. [59] The asynchrony of social effects between individuals greatly distorts collective decisions. ...
... The asynchrony of social effects between individuals greatly distorts collective decisions by introducing social biasing effects including herding and snowballing [59]. The herding effect indicates that an individual in a group follows the opinions of the population majority. ...
Collective intelligence (CI) refers to the intelligence that emerges at the macro-level of a collection and transcends that of the individuals. CI is a continuously popular research topic that is studied by researchers in different areas, such as sociology, economics, biology, and artificial intelligence. In this survey, we summarize the works of CI in various fields. First, according to the existence of interactions between individuals and the feedback mechanism in the aggregation process, we establish CI taxonomy that includes three paradigms: isolation, collaboration and feedback. We then conduct statistical literature analysis to explain the differences among three paradigms and their development in recent years. Second, we elaborate the types of CI under each paradigm and discuss the generation mechanism or theoretical basis of the different types of CI. Third, we describe certain CI-related applications in 2019, which can be appropriately categorized by our proposed taxonomy. Finally, we summarize the future research directions of CI under each paradigm. We hope that this survey helps researchers understand the current conditions of CI and clears the directions of future research.
... The effectiveness of surveys and polls is thus limited to capture the average sentiment that characterizes a population but no collective intelligence can emerge from such methods. [3] Even in cases where there are methods for people to influence each other, like Reddit, this is done asynchronously. The issue with this type of asynchrony is the introduction of social biasing, also known as herding or snowballing effects. ...
... The threshold for reaching a decision in both brains and honeybee swarms is not the unanimous excitation of units, or even a simple majority, but often just a sufficient quorum of excitation. [3] Within biological brains, integrator neurons act to sum the activation among supportive units while inhibiting the activation of competing units. This combination of activation and inhibition helps avoid deadlocks and promote optimal decisions. ...
... Artificial Swarm Intelligence (ASI) is an emerging area of human intellect study derived from nature's phenomenon of large groups that form real-time closedloop systems with continuous feedback to converge on solutions together, such as bees swarming or fish schooling (Couzin, 2008;Marchall et al., 2009;Seeley, & Visscher, 2003). Unlike Swarm Intelligence (SI) that focuses on the development of autonomous drones or simulated agents, ASI seeks to amplify human intellect by networking groups of humans in a closed-loop system that can answer questions, make predictions, reach decisions, or take actions with greater accuracy and optimized satisfaction among participants (Rosenberg, 2015). ...
... Among its published successes, Swarm AI technology was used to predict the 2015 Academy Awards with a 73% success rate using a swarm comprised of seven individuals randomly selected from a group of 48 movie fans. In comparison, the average participant in the larger group had a 40% success rate, and a standard poll (frequency of responses) of the individuals produced only a 47% success rate (Rosenberg, 2015). A series of follow-up studies published in 2016 and 2017 explore the impacts of novice vs expert swarms, small vs large sample sizes, and the results of individuals vs swarm on polling accuracy using Swarm AI technology. ...
Ratings provided by Pilots on workload scales and usability surveys can be biased by subjective differences in perception, experience, skill, emotional state, motivation, and estimation of risk/cost that may be associated with performing a task. Personality dynamics can further compound polarization of issues during pilot debriefings. What if these unwanted effects could be filtered out of pilot data collection and we could cost-effectively access a higher-order, collective ‘pilot brain’ made up of a combined pilot intellect, intuition, and experience to provide more accurate insight into workload and usability? Swarm AI technology was used in a high fidelity pilot simulation event and compared against a traditional methodology for collecting workload and usability survey data. Pilot and Subject Matter Expert workload and usability survey ratings were collected during the event and compared to a post-event pilot swarm. The results of the study showed pilots engaging in collective intelligence were found to be more effective at rating workload, and also more aligned with Subject Matter Expert workload ratings. This initial workload testing suggests that Swarm AI technology and techniques have great potential for usability research by activating the collective intelligence of groups, which can exceed that of the individual performing alone. The usability survey sample was limited, therefore further study is recommended to validate the generalizability of this technology to Likert Scale data.
... To answer this question, researchers have looked to Mother Nature for guidance, finding that many species have evolved methods for tapping the intelligence of groups [5,6,7,8,9,13]. What nature does not do is collect independent samples and then aggregate the data after the fact, the way Galton did in his famous experiment. Instead, nature forms real-time closed-loop systems with continuous feedback, enabling large groups to work in synchrony and converge on solutions together. ...
... After all, the results of this study parallel the benefits of swarming among honeybees and other social organisms, where the decisions are reached in real-time synchrony as closed-loop dynamic systems [5,12]. In fact, the swarming algorithms used by the UNU platform on which this study was run, were modeled specifically after the decision making processes of honeybees [7,13], so it's reasonable to expect a similar amplifications of intelligence. ...
For well over a century, researchers in the field of Collective Intelligence have shown that groups can outperform individuals when making decisions, predictions, and forecasts. The most common methods for harnessing the intelligence of groups treats the population as a “crowd” of independent agents that provide input in isolation in the form of polls, surveys, and market transactions. While such crowd-based methods can be effective, they are markedly different from how natural systems harness group intelligence. In the natural world, groups commonly form real-time closed-loop systems (i.e. “swarms”) that converge on solutions in synchrony. The present study compares the predictive ability of crowds and swarms when tapping the intelligence of human groups. More specifically, the present study tasked a crowd of 469 football fans and a swarm of 29 football fans in a challenge to predict 20 Prop Bets during the 2016 Super Bowl. Results revealed that the crowd, although 16 times larger in size, was significantly less accurate (at 47% correct) than the swarm (at 68% correct). Further, the swarm outperformed 98% of the individuals in the full study. These results suggest that swarming, with closed-loop feedback, is potentially a more effective method for tapping the insights of groups than traditional polling.
... Our method can also be applied in human resources management to assess a team's performance in a group interview or a group discussion. What's more, in "social swarming" platforms (Rosenberg 2015), where groups of people contribute to a decision-making process simultaneously and collectively to achieve better performance than individual decisions, our method can also be applied to analyze the dynamics of the whole process as well as the contribution of each user. ...
Generating options is crucial to making good decisions. Prior research has designed experiments to investigate how different interventions (e.g., value-focused brainstorming) affect the quantity and quality of the generated options. We propose a novel empirical method that characterizes the group option-generation process in two steps: first is to use natural language processing to represent the cognitive space of a group based on their conversation transcripts; second is to assess the discussion dynamics, e.g., inclinations of exploration versus exploitation, with a multi-dimensional Hawkes process. By applying the representation and modeling method to the brainstorming stage of a high-school product design contest, we identify three reference types of group decision-makers – mechanic, propeller, and thinker, and estimate each team participating in the context as a mixture of the three types. We further conduct model-based analysis on how the mixing configuration affects team performance in terms of their navigation strategies in the cognitive space. Finally, we report a case study on applying the proposed method to test a particular intervention, i.e., asking subjects to think about objectives beforehand, in a brainstorming exercise discussing solutions to improve student life satisfaction at our university.
... The collective decision-making process found in honey bee swarms provides a powerful example of how groups, working together in closed-loop systems, can significantly amplify their combined intellect [Marshall et al. 2009] and inspired the development of Swarm AI-a collaborative intelligence technology that enables networked human groups to make decisions by working together in systems modeled on natural swarms. Using the Swarm AI platform, each participant contributes their unique knowledge and perspectives in parallel until the group converges upon optimized decision [Rosenberg 2015]. By enabling human groups to converge synchronously on solutions in realtime, Swarm AI has been shown to amplify the combined intelligence of teams so that they produce more accurate forecasts [Rosenberg et al. 2016], generate better informed market research and human resource decisions, and surpass machine learning approaches to diagnosing medical conditions [Halabi 2018]. ...
Group decision-making is strengthened by the varied knowledge and perspectives that each member brings, yet teams often fail to capitalize on their diversity. This paper describes how Swarm AI, a novel collaborative intelligence technology modeled on the decision-making process of honey bee swarms, enables networked human groups to more effectively leverage their combined insights. Through an empirical study conducted on 60 small teams, each of 3 to 6 members, we demonstrate the capacity of Swarm AI to significantly amplify the collective intelligence of human groups. A well-known testing instrument—the Reading the Mind in the Eyes (RME) test —was used to measure the social intelligence of each team—a key indicator of collective intelligence. The study compares the RME performance of (i) individuals, (ii) teams working by majority vote, and (iii) teams using an interactive software platform that employs Swarm AI technology.
... This has empowered the group to thrive, over the individual, and as such has provided numerous possibilities for study, which have given consistent results in supporting the collaborative vote hypothesis. [7][10] [11] [12] Our system provides a better solution to the decision making process by taking into account the first vote bias which is present in the already existing platforms, such as Reddit. As such we have developed a solution that is based on collaboration with the biggest role being played by the most experienced members and by the overall vote statistics and time limitations. ...
... The Significant results have been obtained in the design of Multi-Robot Systems by applying computational models based on Swarm Intelligence [11,12] which by definition ensures a concurrent / parallel data processing as a result of which the most optimal decision and performance solutions are obtained. [13,14,15]. ...
... The Integrated Decision Making Platform components have been designed based on the human anatomy and various naturally occurring phenomena and processes like swarming [4] and the human neural decision making process. The human body configures itself based on personal experience and adjusts its reaction to the outside world after each decision you make, storing what it learns using the epigenome, which is involved in regulating gene expression and development. ...
This article describes an Integrated Decision Making Environment modeled after the human anatomy and various naturally occurring phenomena and processes, like swarming, and the human neural decision making process. Observing the natural fractals related to decision making, which can be clearly seen in bee swarms, ant colonies and human neurons, together with the organic capabilities of storing and distributing information using the DNA and the overall anatomy of the human body, we want to define a coherent organic Decision Making Environment custom-tailored for the human society by nature itself. In order to properly replicate the organic operating system used by the human body to govern itself we must first start looking at how data is distributed between cells and stored inside the DNA. In order to reproduce this kind of distributed and decentralized database we decided to use Blockchain technology as it shares a lot of the key properties with human DNA.
... Similar results have been shown in swarms as small as 4 people to swarms as large as 30 people [1][2][3][4][5][6][7][28][29][30][31][32][33], so swarms can amplify the accuracy of both small and large groups. These and other results support the view that swarming, with closed-loop feedback, is a far more efficient method for harnessing group insights than polling, even when polls target significantly larger populations. ...
Swarm Intelligence (SI) is a natural phenomenon that enables social species to quickly converge on optimized group decisions by interacting as real-time closed-loop systems. This process, which has been shown to amplify the collective intelligence of biological groups, has been studied extensively in schools of fish, flocks of birds, and swarms of bees. This paper provides an overview of a new collaboration technology called Artificial Swarm Intelligence (ASI) that brings the same benefits to networked human groups. Sometimes referred to as "human swarming" or building "hive minds," the process involves groups of networked users being connected in real-time by AI algorithms modeled after natural swarms. This paper presents the basic concepts of ASI and reviews recently published research that shows its effectiveness in amplifying the collective intelligence of human groups, increasing accuracy when groups make forecasts, generate assessments, reach decisions, and form predictions. Examples include significant performance increases when human teams generate financial predictions, business forecasts, subjective judgments, and medical diagnoses.
... Similar results have been shown in swarms as small as 4 people to swarms as large as 30 people [1][2][3][4][5][6][7][29][30][31][32][33][34], so swarms can amplify the accuracy of both small and large groups. These and other results support the view that swarming, with closed-loop feedback, is a far more efficient method for harnessing group insights than polling, even when polls target significantly larger populations. ...
Swarm Intelligence (SI) is a natural phenomenon that enables social species to quickly converge on optimized group decisions by interacting as real-time closed-loop systems. This process, which has been shown to amplify the collective intelligence of biological groups, has been studied extensively in schools of fish, flocks of birds, and swarms of bees. This paper provides an overview of a new collaboration technology called Artificial Swarm Intelligence (ASI) that brings the same benefits to networked human groups. Sometimes referred to as "human swarming" or building "hive minds," the process involves groups of networked users being connected in real-time by AI algorithms modeled after natural swarms. This paper presents the basic concepts of ASI and reviews recently published research that shows its effectiveness in amplifying the collective intelligence of human groups, increasing accuracy when groups make forecasts, generate assessments, reach decisions, and form predictions. Examples include significant performance increases when human teams generate financial predictions, business forecasts, subjective judgments, and medical diagnoses.
... Bayesian network [31], hurricane optimization algorithm [32], gravitational search algorithm [33], human swarming [34], bat algorithm [35], and stochastic diffusion search [36] are proposed, inspired by the phenomenon of nature for solving larger problems in science and technology in recent years. Hence, soft computing has received noteworthy attention and has been applied to a range of real-world problems. ...
This paper introduces two novel concepts, the one-variable update mechanism (UM1) and the harmonic step-length strategy (HSS), to improve the continuous simplified swarm optimization (SSO). The proposed UM1 and HSS are able to balance the exploration and exploitation ability of the continuous SSO in solving high-dimensional multivariable and multimodal numerical continuous benchmark functions. The proposed new update mechanism UM1 updates only one variable and it is total completely different to that in the SSO without needing to update all variables. In the proposed UM1, the HSS enhances exploitation capacity by decreasing the step-length based on a harmonic sequence. Numerical experiments are performed on 18 high-dimension functions to confirm the efficiency of the proposed approach.
... Even more, it has been shown that when users can influence each other but still in an asynchronous way, the group decisions are distorted by social biasing effects [48]. Recently, it has been proposed that the use of structures similar to natural swarms can correct some of these problems [49]. Indeed, by allowing users to participate in decision making processes in real time with feedback about what the rest is doing, in some sort of human swarm, it is possible to explore more efficiently the decision space and reach more accurate predictions than with simple majority voting [50]. ...
Despite many efforts, the behavior of a crowd is not fully understood. The advent of modern communication means has made it an even more challenging problem, as crowd dynamics could be driven by both human-to-human and human-technology interactions. Here, we study the dynamics of a crowd controlled game (Twitch Plays Pokémon), in which nearly a million players participated during more than two weeks. Unlike other online games, in this event all the players controlled exactly the same character and thus it represents an exceptional example of a collective mind working to achieve a certain goal. We dissect the temporal evolution of the system dynamics along the two distinct phases that characterized the game. We find that having a fraction of players who do not follow the crowd’s average behavior is key to succeed in the game. The latter finding can be well explained by an nth order Markov model that reproduces the observed behavior. Secondly, we analyze a phase of the game in which players were able to decide between two different modes of playing, mimicking a voting system. We show that the introduction of this system clearly polarized the community, splitting it in two. Finally, we discuss one of the peculiarities of these groups in the light of the social identity theory, which appears to describe well some of the observed dynamics.
... Unanimous AI has developed a freely available online collaborative platform called UNU that enables individuals to work together as a group and leverage their collective intelligence. The UNU platform has been used previously to examine the performance of groups and collective intelligence (Rosenberg, 2015). ...
Every year millions of people fill out brackets, trying to accurately predict the outcome of the NCAA Men’s Basketball March Madness tournament. This study examines how collective swarm intelligence might impact these choices in small groups. Rather than working by themselves, groups of people came together to combine their knowledge and opinions and pick brackets collectively. It is generally agreed that collective intelligence is effective in decision-making. However, how and why collective intelligence augments performance has not been totally agreed upon, and the theoretical explanations have been elusive. This study examines groups that are either highly dedicated or high in expertise to see whether they perform differently based on these dimensions.
... Ant-based routing [13] Crowd simulation [24] Human swarming [34] Swarmic art [9] III. Proposed Method ...
Community detection is the fundamental issue in the social network analysis. In present scenario, complex network analysis is sizzling research area. In this paper, we proposed the new idea of finding the fuzzy community detection in social network with the help of permanence concept with node similarity based genetic algorithm. In this proposed work we found the both disjoint community and the overlapping community detection then we compares it as quality wise and accuracy wise with the help of some functions. We utilized the disjoint community structure as an input for our base algorithm. We employed the artificial datasets and the real world datasets for our experiment. In this paper we compare the disjoint community and the fuzzy community for our proposed algorithm with the help of some quality and accuracy based parameters. The proposed method eliminates the need of a separate algorithm for fuzzy community structures. It fulfills the role of both disjoint community detection and fuzzy community detection without adding any extra step of genetic algorithm.
... Even more, it has been shown that when users can influence each other but still in an asynchronous way, the group decisions are distorted by social biasing effects [38]. Recently, it has been proposed that the use of structures similar to natural swarms can correct some of these problems [39]. Indeed, by allowing users to participate in decision making processes in real time with a feedback about what the rest is doing, in some sort of human swarm, it is possible to explore more efficiently the decision space and reach more accurate predictions than with simple majority voting [40]. ...
Despite many efforts, the behavior of a crowd is not fully understood. The advent of modern communication media has made it an even more challenging problem, as crowd dynamics could be driven by both human-to-human and human-technology interactions. Here, we study the dynamics of a crowd controlled game (Twitch Plays Pok\'emon), in which nearly a million players participated during more than two weeks. We dissect the temporal evolution of the system dynamics along the two distinct phases that characterized the game. We find that players who do not follow the crowd average behavior are key to succeed in the game. The latter finding can be well explained by an n-$th$ order Markov model that reproduces the observed behavior. Secondly, we analyze a phase of the game in which players were able to decide between two different modes of playing, mimicking a voting system. Our results suggest that under some conditions, the collective dynamics can be better regarded as a swarm-like behavior instead of a crowd. Finally, we discuss our findings in the light of the social identity theory, which appears to describe well the observed dynamics.
... Prior research into human swarming has shown that by enabling groups of online users to combine their knowledge, wisdom, insights, and opinions in real-time swarms, enhanced predictions and forecasts can be made. 7,8,9,10,11 Prior research, however, does not address priority setting, which inspires the question: Can real-time swarming be used by groups to converge upon preferred sets of priorities as compared to traditional polls, votes, and surveys? To answer this question, researchers used the UNU swarm intelligence platform to compare priority-setting among diverse groups by vote and by swarm. ...
In the era of digital communication, collective problem solving is increasingly important. Large groups can now resolve issues together in completely different ways, which has transformed the arts, sciences, business, education, technology, and medicine. Collective intelligence is something we share with animals and is different from machine learning and artificial intelligence. To design and utilize human collective intelligence, we must understand how its problem-solving mechanisms work. From democracy in ancient Athens, through the invention of the printing press, to COVID-19, this book analyzes how humans developed the ability to find solutions together. This wide-ranging, thought-provoking book is a game-changer for those working strategically with collective problem solving within organizations and using a variety of innovative methods. It sheds light on how humans work effectively alongside machines to confront challenges that are more urgent than what humanity has faced before. This title is also available as Open Access on Cambridge Core.
In the era of digital communication, collective problem solving is increasingly important. Large groups can now resolve issues together in completely different ways, which has transformed the arts, sciences, business, education, technology, and medicine. Collective intelligence is something we share with animals and is different from machine learning and artificial intelligence. To design and utilize human collective intelligence, we must understand how its problem-solving mechanisms work. From democracy in ancient Athens, through the invention of the printing press, to COVID-19, this book analyzes how humans developed the ability to find solutions together. This wide-ranging, thought-provoking book is a game-changer for those working strategically with collective problem solving within organizations and using a variety of innovative methods. It sheds light on how humans work effectively alongside machines to confront challenges that are more urgent than what humanity has faced before. This title is also available as Open Access on Cambridge Core.
This article explores how a collaboration technology called Artificial Swarm Intelligence (ASI) addresses the limitations associated with group decision making, amplifies the intelligence of human groups, and facilitates better business decisions. It demonstrates of how ASI has been used by businesses to harness the diverse perspectives that individual participants bring to groups and to facilitate convergence upon decisions. It advances the understanding of how artificial intelligence (AI) can be used to enhance, rather than replace, teams as they collaborate to make business decisions.
In the field of image processing, there are several problems where an efficient search of the solutions has to be performed within a complex search domain to find an optimal solution. Multi-thresholding which is a very important image segmentation technique is one of them. The multi-thresholding problem is simply an exponential combinatorial optimization process which traditionally is formulated based on complex objective function criterion which can be solved using only nondeterministic methods. Under such circumstances, there is also no unique measurement which quantitatively judges the quality of a given segmented image. Therefore, researchers are solving those issues by using Nature-Inspired Optimization Algorithms (NIOAs) as alternative methodologies for the multi-thresholding problem. This study presents an up-to-date review on all most important NIOAs employed in multi-thresholding based image segmentation domain. The key issues which are involved during the formulation of NIOAs based image multi-thresholding models are also discussed here.
In the field of image processing, there are several problems where the efficient search has to be performed in complex search domain to find an optimal solution. Image enhancement which improves the quality of an image for visual analysis and/or machine understanding is one of these problems. There is no unique image enhancement technique and it’s measurement criterion which satisfies all the necessity and quantitatively judge the quality of a given image respectively. Thus sometimes proper image enhancement problem becomes hard and takes large computational time. In order to overcome that problem, researchers formulated the image enhancement as optimization problems and solved using Nature-Inspired Optimization Algorithms (NIOAs) which starts a new era in image enhancement field. This study presents an up-to-date review over the application of NIOAs in image enhancement domain. The key issues which are involved in the formulation of NIOAs based image enhancement models are also discussed here.
Much research has been done in the field of collective intelligence to aggregate input from human populations with the goal of amplifying the abilities of the groups. Nearly all prior research follows a similar model where input is collected from human participants in isolation and then aggregated statistically after the fact. The paper introduces a radically different approach in which the human participants is not aggregated statistically, but through a real-time dynamic control in which the participants act, react, and interact as a part of a system modeled after swarms in nature. Early testing of these "human swarms" suggest great potential for amplifying the intelligence of human groups, exceeding traditional aggregation methods.
on the simulation of collaborative
systems as it relates to the emergence of real-time collective intelligence. While theoretical studies are of great research value, there’s a growing need for real-world platforms that test the emergence of collective intelligence among human users. This short paper introduces such a platform. It enables networks of online collaborators to converge on questions, decisions, and dilemmas in real-time, functioning as a unified dynamic system. The dynamic system has been modeled after biological swarms, which is why refer to the process as “social swarming” or "human swarming". Early testing of human swarms suggests a great potential for harnessing collective intelligence.
Follow the Leader?
The Internet has increased the likelihood that our decisions will be influenced by those being made around us. On the one hand, group decision-making can lead to better decisions, but it can also lead to “herding effects” that have resulted in financial disasters. Muchnik et al. (p. 647 ) examined the effect of collective information via a randomized experiment, which involved collaboration with a social news aggregation Web site on which readers could vote and comment on posted comments. Data were collected and analyzed after the Web site administrators arbitrarily voted positively or negatively (or not at all) as the first comment on more than 100,000 posts. False positive entries led to inflated subsequent scores, whereas false negative initial votes had small long-term effects. Both the topic being commented upon and the relationship between the poster and commenter were important. Future efforts will be needed to sort out how to correct for such effects in polls or other collective intelligence systems in order to counter social biases.
We synthesize findings from neuroscience, psychology, and behavioral biology to show that some key features of cognition in
the neuron-based brains of vertebrates are also present in the insect-based swarm of honey bees. We present our ideas in the
context of the cognitive task of nest-site selection by honey bee swarms. After reviewing the mechanisms of distributed evidence
gathering and processing that are the basis of decision making in bee swarms, we point out numerous similarities in the functional
organization of vertebrate brains and honey bee swarms. These include the existence of interconnected subunits, parallel processing
of information, a spatially distributed memory, layered processing of information, lateral inhibition, and mechanisms of focusing
attention on critical stimuli. We also review the performance of simulated swarms in standard psychological tests of decision
making: tests of discrimination ability and assessments of distractor effects.
This study considers the mystery of how the scout bees in a honey bee swarm know when they have completed their group decision making regarding the swarm's new nest site. More specifically, we investigated how the scouts sense when it is appropriate for them to begin producing the worker piping signals that stimulate their swarm-mates to prepare for the flight to their new home. We tested two hypotheses: "consensus sensing," the scouts noting when all the bees performing waggle dances are advertising just one site; and "quorum sensing," the scouts noting when one site is being visited by a sufficiently large number of scouts. Our test involved monitoring four swarms as they discovered, recruited to, and chose between two nest boxes and their scouts started producing piping signals. We found that a consensus among the dancers was neither necessary nor sufficient for the start of worker piping, which indicates that the consensus sensing hypothesis is false. We also found that a buildup of 10–15 or more bees at one of the nest boxes was consistently associated with the start of worker piping, which indicates that the quorum sensing hypothesis may be true. In considering why the scout bees rely on reaching a quorum rather than a consensus as their cue of when to start preparing for liftoff, we suggest that quorum sensing may provide a better balance between accuracy and speed in decision making. In short, the bees appear to begin preparations for liftoff as soon as enough of the scout bees, but not all of them, have approved of one of the potential nest sites.
Stop, Don't Go There
Decision-making in complex brains requires the reaching of a consensus based on information relayed by multiple neurons. A similar situation occurs in the decision-making process of swarming bees, where multiple individuals relay information about suitable hive sites, but a single site is chosen by the swarm. Seeley et al. (p. 108 , published online 8 December; see the Perspective by Niven ) show that consensus in this system is reached as information from scouting bees accumulates within the hive. Stop signals were given by scout bees from a particular site to those scout bees signaling from other sites. As the signals accumulate, scouting ceases and the bees prepare to swarm to the site that was best represented among the scouts. When this process was simulated, the results indicated that cross-inhibition among the bees functions similarly to that which occurs among neurons within complex brains. In both cases, such cross-inhibition prevents the overall system from coming to an impasse.
The choice overload hypothesis states that an increase in the number of options to choose from may lead to adverse consequences such as a decrease in the motivation to choose or the satisfaction with the finally chosen option. A number of studies found strong instances of choice overload in the lab and in the field, but others found no such effects or found that more choices may instead facilitate choice and increase satisfaction. In a meta-analysis of 63 conditions from 50 published and unpublished experiments (N = 5,036), we found a mean effect size of virtually zero but considerable variance between studies. While further analyses indicated several potentially important preconditions for choice overload, no sufficient conditions could be identified. However, some idiosyncratic moderators proposed in single studies may still explain when and why choice overload reliably occurs; we review these studies and identify possible directions for future research. (c) 2010 by JOURNAL OF CONSUMER RESEARCH, Inc..
The problem of how to compromise between speed and accuracy in decision-making faces organisms at many levels of biological complexity. Striking parallels are evident between decision-making in primate brains and collective decision-making in social insect colonies: in both systems, separate populations accumulate evidence for alternative choices; when one population reaches a threshold, a decision is made for the corresponding alternative, and this threshold may be varied to compromise between the speed and the accuracy of decision-making. In primate decision-making, simple models of these processes have been shown, under certain parametrizations, to implement the statistically optimal procedure that minimizes decision time for any given error rate. In this paper, we adapt these same analysis techniques and apply them to new models of collective decision-making in social insect colonies. We show that social insect colonies may also be able to achieve statistically optimal collective decision-making in a very similar way to primate brains, via direct competition between evidence-accumulating populations. This optimality result makes testable predictions for how collective decision-making in social insects should be organized. Our approach also represents the first attempt to identify a common theoretical framework for the study of decision-making in diverse biological systems.
In recent years there has been a growing interest in the relationship
between individual behavior and population-level properties in animal
groups. One of the fundamental problems is related to spatial scale; how
do interactions over a local range result in population properties at
larger, averaged, scales, and how can we integrate the properties of
aggregates over these scales? Many group-living animals exhibit complex,
and coordinated, spatio-temporal patterns which despite their ubiquity
and ecological importance are very poorly understood. This is largely
due to the difficulties associated with quantifying the motion of, and
interactions among, many animals simultaneously. It is on how these
behaviors scale to collective behaviors that I will focus here. Using a
combined empirical approach (using novel computer vision techniques) and
individual-based computer models, I investigate pattern formation in
both invertebrate and vertebrate systems, including - Collective memory
and self-organized group structure in vertebrate groups (Couzin, I.D.,
Krause, J., James, R., Ruxton, G.D. & Franks, N.R. (2002) Journal of
Theoretical Biology 218, 1-11. (2) Couzin, I.D. & Krause, J. (2003)
Advances in the Study of Behavior 32, 1-75. (3) Hoare, D.J., Couzin,
I.D. Godin, J.-G. & Krause, J. (2003) Animal Behaviour, in press.) -
Self-organized lane formation and optimized traffic flow in army ants
(Couzin, I.D. & Franks, N.R. (2003) Proceedings of the Royal Society
of London, Series B 270, 139-146) - Leadership and information transfer
in flocks, schools and swarms. - Why do hoppers hop? Hopping and the
generation of long-range order in some of the largest animal groups in
nature, locust hopper bands.
Cooperation is central to human social behaviour. However, choosing to cooperate requires individuals to incur a personal cost to benefit others. Here we explore the cognitive basis of cooperative decision-making in humans using a dual-process framework. We ask whether people are predisposed towards selfishness, behaving cooperatively only through active self-control; or whether they are intuitively cooperative, with reflection and prospective reasoning favouring 'rational' self-interest. To investigate this issue, we perform ten studies using economic games. We find that across a range of experimental designs, subjects who reach their decisions more quickly are more cooperative. Furthermore, forcing subjects to decide quickly increases contributions, whereas instructing them to reflect and forcing them to decide slowly decreases contributions. Finally, an induction that primes subjects to trust their intuitions increases contributions compared with an induction that promotes greater reflection. To explain these results, we propose that cooperation is intuitive because cooperative heuristics are developed in daily life where cooperation is typically advantageous. We then validate predictions generated by this proposed mechanism. Our results provide convergent evidence that intuition supports cooperation in social dilemmas, and that reflection can undermine these cooperative impulses.
Cellular Robotic Systems are capable of ’intelligent* behavior. The meaning of this intelligence is analyzed in the paper.
We define robot intelligence and robot system intelligence in terms of unpredictability of improbable behavior. The concept
of unpredictability is analyzed in relation to (1) statistical unpredictability, (2) inaccessibility, (3) undecidability,
(4) intractability, and (5) non-representability. We argue that the latter two type of unpredictability, when exhibited by
systems capable of producing order, can result in a non-trivial, different form of intelligent behavior (Swarm Intelligence).
Engineering problems related to Swarm Intelligence are mentioned in relation to Cellular Robotic Systems which consist of
collections of autonomous, non-synchronized, non-intelligent robots cooperating to achieve global tasks.
Equilibrium analysis of a prediction market when traders have heterogeneous
priors and private information. When agents can invest a limited
amount of money in this market (or their absolute risk aversion is
decreasing in wealth), the equilibrium price under-reacts to information.
The remarkable collective action of organisms such as swarming ants, schooling fish and flocking birds has long captivated the attention of artists, naturalists, philosophers and scientists. Despite a long history of scientific investigation, only now are we beginning to decipher the relationship between individuals and group-level properties. This interdisciplinary effort is beginning to reveal the underlying principles of collective decision-making in animal groups, demonstrating how social interactions, individual state, environmental modification and processes of informational amplification and decay can all play a part in tuning adaptive response. It is proposed that important commonalities exist with the understanding of neuronal processes and that much could be learned by considering collective animal behavior in the framework of cognitive science.
We analyze a binary prediction market in which traders have heterogeneous prior beliefs and private information. Realistically, we assume that traders are allowed to invest a limited amount of money (or have decreasing absolute risk aversion). We show that the rational expectations equilibrium price underreacts to information. When favorable information to an event is available and is revealed by the market, the price increases and this forces optimists to reduce the number of assets they can (or want to) buy. For the market to equilibrate, the price must increase less than a posterior belief of an outside observer.
On optimal decision making in brains and social insect colonies
- J A R Marchall
- R Bogacz
- A Dornhaus
- R Planque
- T Kovacs
- N R Franks
J.A.R. Marchall, R. Bogacz, A. Dornhaus, R. Planque,
T.Kovacs, N.R. Franks, On optimal decision making in brains
and social insect colonies, J.R. Soc Interface 6,1065 (2009).