Collective Intelligence

Goldstone and Gureckis (2009): "Collective behavior" link
Pratt (2010): "Collective Intelligence" pdf
Salminen (2012): "Collective Intelligence in Humans: A Literature Review" pdf

Aggregation

Abernethy et al. (2014): "Information Aggregation in Exponential Family Markets" pdf
*Bonnefon (2007): "How Do Individuals Solve the Doctrinal Paradox in Collective Decisions? An Empirical Investigation" link
*Bonnefon (2011): "The Doctrinal Paradox, a New Challenge for Behavioral Psychologists" link
Carvalho and Larson (2013): "A Consensual Linear Opinion Pool" pdf
Chen and Pennock (2005): "Information Markets vs. Opinion Pools: An Empirical Comparison" pdf
Cruise and Ganesh (2013): "Probabilistic consensus via polling and majority rules" pdf
Dietrich (2008): "Judgment aggregation: a general theory of collective decisions" pdf
Dietrich and List (2011): "Propositionwise judgment aggregation: the general case" pdf
Barbara Drossel (1998): "A simple model for the formation of a complex organism" pdf
Endriss et al. (2014): "Complexity of Judgment Aggregation" link
Ernest Forman and Kirti Peniwatib (1998): "Aggregating individual judgments and priorities with the analytic hierarchy process" link
Grossi (2009): "Unifying Preference and Judgment Aggregation" pdf
Herzberg (2014): "Aggregating infinitely many probability measures" pdf
*Hylland and Zeckhauser (1979): "The impossibility of Bayesian group decision making with separate aggregation of beliefs and values" pdf
William F. Lawless and Donald A. Sofge (2011): "The Mathematics of Aggregation, Interdependence, Organizations and Systems of Nash Equilibria: A Replacement for Game Theory" pdf
Lee et al. (2014): "A cognitive model for aggregating people's rankings" pdf
summary
In which a Bayesian approach to aggregating rankings inspired by a Thurstonian theory of cognitive generation of rankings is given. Rankings are induced by a single latent-dimensional representation, which individuals have noisy observations of. Noise in individual observations generates heterogeneity in rankings.

List (2008): "Judgment aggregation: a bibliography on the discursive dilemma, doctrinal paradox and decisions on multiple propositions" link
*List (2011): "The theory of judgment aggregation: An introductory review" pdf
List and Pettit (2002): "AGGREGATING SETS OF JUDGMENTS: AN IMPOSSIBILITY RESULT" pdf
*Maynard-Zhang and Lehmann (2003): "Representing and Aggregating Conflicting Beliefs" pdf
*Pennock and Wellman (1997): "Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market" pdf
Pivato (2008): "The Discursive Dilemma and Probabilistic Judgement Aggregation" pdf
Rahwan and Tohme (2010): "Collective Argument Evaluation as Judgement Aggregation" pdf
Reid et al. (2014): "Generalised Mixability, Constant Regret, and Bayesian Updating" pdf
Roback and Givens (2000): "Supra-Bayesian Pooling of Priors Linked by a Deterministic Simulation Model" pdf
Sauper and Barzilay (2014): "Automatic Aggregation by Joint Modeling of Aspects and Values" link
Seckarova (2010): "Supra-Bayesian Approach to Merging of Incomplete and Incompatible Data" pdf
Seidenfeld et al. (1989): "On the Shared Preferences of Two Bayesian Decision Makers" pdf
H. Van Dyke Parunak et al. (2013): "Characterizing and aggregating agent estimates" link
Venanzi et al. (2014): "Community-Based Bayesian Aggregation Models for Crowdsourcing" pdf
*Volk (2000): "Competitive on-line statistics" pdf

Collective Action

Bruggeman (2013): "Solidarity, synchronization and collective action" link
Comfort and Haase (2006): "COMMUNICATION, COHERENCE, AND COLLECTIVE ACTION: The Impact of Hurricane Katrina on Communications Infrastructure" pdf
Hill (2013): "Almost Wikipedia: Eight Early Encyclopedia Projects and the Mechanisms of Collective Action" pdf
summary
This paper compares the mechanisms used by wikipedia to several other less successful online collaborative encyclopedia project architectures to understand why wikipedia was so successful. The paper suggests that wikipedia had a recognizable goal, a low barrier to entry for contributions, and a low emphasis on individual recognition, and that its success was not due to coming at the right time or having better technology. The paper uses traditional sociological methods to come to these conclusions: a comparitive multiple case study and iterative step-wise coding of the interviews.

Kisolo-Ssonko (2014): "On Collective Action" pdf

Collective Agency

Bouchard et al. (2014): "On the emergence of an "intention field" for socially cohesive agents" link
Fan et al. (1998): "Reasoning about team tracking" link
Gardner and Grafen (2009): "Capturing the superorganism: a formal theory of group adaptation" link
List (2005): "Group knowledge and group rationality: a judgment aggregation perspective" pdf
List and Pettit (2005): "On the Many as One" pdf
List and Pettit (2006): "Group Agency and Supervenience" pdf
*Macy (1997): "Identity, interest and emergent rationality" pdf
Pettit (2007): "Rationality, Reasoning and Group Agency" link
*Risse (2003): "Bayesian Group Agents and Two Modes of Aggregation" pdf
Wooldridge (1994): "Coherent Social Action" pdf

Collective Animal Behavior

Ame et al. (2006): "Collegial decision making based on social amplification leads to optimal group formation" pdf
summary
Typical analyses of ideal group sizes are based on simplifying assumptions, such as every individual knowing the quality of all options and there being no influence of social information. This paper presents a model for how cockroaches decide on nest sites that incorporates these factors. Cockroaches appear to alternate between resting and exploring. They will rest for longer at popular sites but also be less likely to explore popular sites. The models makes some predictions that are empirically validated: even splits between saturated sites, even splits between sites that cannot contain everyone but are greater than half the size of the population, and consensus on one site that can contain the entire population. This solution is generalized with more than two sites to prefering the largest possible even splits when possible. This solution is driven by the preference for resting longer in sites with more conspecifics. The authors show this solution is an optimal balance of benefits of group cohesiveness with competition for resources, thus maximizing individual fitness.

Behrend and Bitterman (1961): "Probability-Matching in the Fish" link
summary
This paper presents an early study on probability matching in fish, which the paper claims was the only other animal that had been shown to probability match at the time. Along with a couple other findings, the paper shows, consistent with a previous study by the same lab, that probability matching in this species of fish occurs if corrective guidance is given for incorrect decisions but maximization occurs otherwise. The interpretation of probability matching here seems a bit suspect, though, since it could possibly be that the fish learn the causal relationship between choosing the wrong option and being presented shortly thereafter with the opportunity to choose the correct option. The only mystery then is why matching occurs in this task in fish but not in rats.

*Bialek et al. (2014): "Social interactions dominate speed control in poising natural flocks near criticality" link
summary
This paper uses a maximum entropy model to examine a hypothesis that bird flocks exist near a critical point. Near this critical point information is communicated at long ranges across the flock, allowing the flock to act like a single unit. While this hypothesis has been offered previously, this paper actually fits real bird data to the model.

Bottinelli et al. (2014): "How Do Fish Use the Movement of Other Fish to Make Decisions?" link
Bshary et al. (2014): "Social cognition in fishes" link
summary
This paper argues that fish might be a good model species for studying social cognition on the basis of anatomical and behavioral similarity to other vertebrate species.

Conradt (2012): "Models in animal collective decision-making: information uncertainty and conflicting preferences" link
summary
This paper reviews models used for collective decision-making in animal groups with individuals that have uncertain information or conflicting preferences. The review of models incorporating just uncertain information is brief, touching on models without quorum models and models for collective motion. The review of models for conflicting preferences is more extensive, including discussion of both group-level and individual-level models of synchronization of decision times with different models for various group sizes. There were few to no models at the time the article was written incorporating both information uncertainty and conflicting preferences. The paper also notes that many empirical studies report unshared or dictatorial decision-making.

Conradt (2013): "Collective animal decisions: preference conflict and decision accuracy" link
Conradt and Roper (2003): "Group decision-making in animals" pdf
Conradt and Roper (2005): "Consensus decision making in animals" pdf
Conradt and Roper (2009): "Conflicts of interest and the evolution of decision sharing" link
Conradt et al. (2013): "Swarm Intelligence: When Uncertainty Meets Conflict" link
DeLellis et al. (2014): "Collective behaviour across animal species" link
Dell et al. (2014): "Automated image-based tracking and its application in ecology" link
Detrain and Deneubourg (2006): "Self-organized structures in a superorganism: do ants behave like molecules?" link
Edwards and Pratt (2009): "Rationality in collective decision-making by ant colonies" pdf
Franks et al. (2002): "Information Flow, opinion polling and collective intelligence in house-hunting social insects" pdf
Grunbaum (2012): "A spatially explicit Bayesian framework for cognitive schooling behaviours" link
Katsikopoulos and King (2010): "Swarm Intelligence in Animal Groups: When Can a Collective Out-Perform an Expert?" link
King and Cowlishaw (2007): "When to use social information: the advantage of large group size in individual decision making" pdf
List (2004): "Democracy in animal groups: a political science perspective" link
List and Vermeule (2013): "Independence and Interdependence: Lessons from the Hive" pdf
Marshall et al. (2009): "On optimal decision-making in brains and social insect colonies" link
*Perez-Escudero et al. (2013): "Estimation models describe well collective decisions among three options" link
Pinter-Wollman et al. (2013): "The dynamics of animal social networks: analytical, conceptual, and theoretical advances" pdf
Ratzke and Gore (2015): "Shaping the Crowd: The Social Life of Cells" pdf
summary
This paper gives a preview of an interesting sounding piece of work. The work they describe shows that if individual cells in a society of cells can modulate the distances their communications reach in a society, rich collective behaviors can emerge.

Ramdya et al. (2014): "Mechanosensory interactions drive collective behaviour in Drosophila" pdf
summary
This paper consists of an exceptionally detailed evaluation of the mechanisms of collective response in Drosophila to the presence of CO2. The paper shows that groups of flies are better to able to avoid this odor than individuals and that the collective response consists of a combination of three mechanisms: more individual walking in the presence of CO2, increased likelihood of individuals retreating when entering CO2 air, and increased walking in the proximity of others. The researchers also identifed the neuronal and genetic basis for the third mechanism, which allowed them to directly test whether this encounter response caused the collective response by knocking out the appropriate gene. Further, the researchers showed that mutant flies that could not sense CO2 still responded to its presence because of the collective reponse.

*Sasaki and Pratt (2011): "Emergence of group rationality from irrational individuals" link
Sasaki and Pratt (2012): "Groups have a larger cognitive capacity than individuals" link
Schaerf et al. (2013): "Do small swarms have an advantage when house hunting? The effect of swarm size on nest-site selection by Apis mellifera" link
*Strandburg-Peshkin et al. (2013): "Visual sensory networks and effective information transfer in animal groups" link
Strandburg-Peshkin et al. (2015): "Shared decision-making drives collective movement in wild baboons" link
summary
Using GPS data from troops of wild baboons, this paper argues that movement in these groups is determined predominantly by shared decision-making rather than hierarchical decisions. There were also no gender differences among the baboons in terms of influence over group decision-making, despite males being dominant in social interactions in this species.

Sueur et al. (2012): "Collective decision-making and fission–fusion dynamics: a conceptual framework" pdf
*Sumpter et al. (2012): "Six Predictions about the Decision Making of Animal and Human Groups" link
Torney et al. (2009): "Context-dependent interaction leads to emergent search behavior in social aggregates" link
summary
This paper introduces a model of collective behavior that can generate gradient following behavior without individuals having the ability to sense gradients. The environment model is a chemical flow in a surrounding current, with individuals movements directed by their directions and the current. As is standard, the authors assume individual direction is determined by a combination of alignment, repulsion, and attraction to neighbors. However, individuals also vary their zones of alignment, repulsion, and attraction based on the surrounding environment. Individuals asses their confidence in their current direction by looking at the ratio of the current concentration to the maximum discounted concentration observed in the past. (Note: How is this not an estimate of the gradient?) Individuals then reduce their alignment and attraction zones, relying on social information less, if they are in better areas and increase it, relying on social information more, if they are in worse areas. Interestingly, this model displays nonmonotonic effects of group size, where once a group gets too large, it tends to break up. The paper argues that this could be a general model for many species and alluded to evidence for changing attraction and alignment zones observed in fish.

Wallraff (1978): "Social interrelations involved in migratory orientation of birds: Possible contribution of field studies" link
Ward et al. (2011): "Fast and accurate decisions through collective vigilance in fish shoals" link

Collective Belief

List (2014): "Three kinds of collective attitudes" pdf
summary
This paper argues that there are three types of collective beliefs: aggregate, common, and corporate. Aggregate attitudes are formed by statistical or algorithmic combination of individual attitudes. Common attitudes are formed by common knowledge in a group of holding a particular belief. Corporate attitudes play the functional role of beliefs in group agents. Only this last type is involved in group agency.

*Moussaid et al. (2013): "Social Influence and the Collective Dynamics of Opinion Formation" link
Moussaid et al. (2015): "The amplification of risk in experimental diffusion chains"
summary
Using a gossip/telephone-game-like experimental design where chains of people communicate information sequentially, the researchers show that sequential private communication reduces message accuracy and amplifies risk perception (by not passing on positive statement and by emphasizing negative statements). The authors propose a simple model of this process, and show through simulations that even completely unbiased initial messages will be skewed by their chains. The authors also argue that the distortion is influenced by the preconceptions of participants. (PK: It would be interesting to see, e.g., how this applies to perception of evidence in scientific discovery. Not clear if that has been studied previously. The authors seem to focus on studying risk as their novel contribution, so it's possible lots of variants of their paradigm have already been examined.)

Watts and Dodds (2007): "Influentials, Networks, and Public Opinion Formation" link

Collective Cognition

Bahrami et al. (2012): "What failure in collective decision-making tells us about metacognition" link
Clement et al. (2013): "Collective Cognition in Humans: Groups Outperform Their Best Members in a Sentence Reconstruction Task" pdf
Dale et al. (2011): "How two people become a tangram recognition system" pdf
Estrada and Vargas-Estrada (2013): "Peer Pressure Shapes Consensus, Leadership, and Innovations in Social Groups" link
Fuge et al. (2013): "Network Analysis of Collaborative Design Networks: A Case Study of OpenIDEO" link
Gunasekaran (2013): "The emergence of collective intelligence" link
summary
This paper suggests (somewhat contrary to previous work) that individual intelligence is important for collective intelligence. The authors argue that it is important for individuals to be aware of their intelligence and skills. The authors propose that collective intelligence emerges from cycles of "idea-disagreement-reasoning-counter-idea-reasoning-agreement". The authors developed this model by observing videos of groups of individuals interacting in a group decision-making context. The paper provides few details about these videos, no formalization of their model, and no rigorous support for their hypothesis that individual intelligence affects collective intelligence.

Gureckis and Goldstone (2006): "Thinking in groups" link
Lazar and Friedman (2007): "The Network Struture of Exploration and Exploitation" pdf
Levitt and March (1988): "Organizatinal Learning" pdf
List (2005): "Distributed Cognition: A Perspective from Social Choice Theory" pdf
Page (2014): "Where diversity comes from and why it matters?" link
Pentland (2006): "Collective Intelligence" link
summary
This paper was an interesting find. It mostly is a review of Sandy's work on sociometric badges, all of which I believe is covered more famously in his Honest Signals book. However, the intro contains a collective intelligence/collective cognition perspective, even coming close to downward causality claims, that I don't recall being so prominent in Honest Signals. I hadn't realized that Sandy had written explicitly about this topic, particularly in such an early paper.

Sasaki (2014): "Precis of Psychology Of A Superorganism" pdf
summary
This summary of a prize-winning thesis discusses the emergence of collective rationality/cognition in groups of Temnothorax ants. It's a nice review of four papers by Sasaki and Pratt. The author shows that individual ants are able to distinguish the qualities of pairs of nest sites, then shows they are individually susceptible to decoy biases but not collectively susceptible. Next, the author showed that ants together have a larger collective cognitive capacity than individual ants by presenting individual and colonies with either two or eight nest sites. The third paper shows ant colonies can make worse decisions than individuals when the number of choices is small and the difference in quality between choices is small. Finally, they showed that colonies collective learn which of two attribtues of nest sites are more informative about nest quality using a clever experimental design that required the ants to remember which of the two attributes had been associated with an inferior nest in a previous phase.

Singh et al. (2011): "How important is team structure to team performance?" link
Singh et al. (2012): "Computational studies to understand the role of social learning in team familiarity and its effects on team performance" link
*Singh et al. (2012): "Social learning in design teams: The importance of direct and indirect communications" link
*Singh et al. (2013): "DEVELOPING A COMPUTATIONAL MODEL TO UNDERSTAND THE CONTRIBUTIONS OF SOCIAL LEARNING MODES TO TASK COORDINATION IN TEAMS" pdf
Smart et al. (2009): "Collective Cognition: Exploring the Dynamics of Belief Propagation and Collective Problem Solving in Multi-Agent Systems" link
Smart et al. (2013): "Exploring the Dynamics of Collective Cognition Using a Computational Model of Cognitive Dissonance" link
Sornette et al. (2014): "How Much is the Whole Really More than the Sum of its Parts? 1 + 1 = 2.5: Superlinear Productivity in Collective Group Actions" pdf
summary
This paper provides evidence for synergistic performance in open-source software teams, where team output increase superlinearly with group size. The paper offers a model for this superlinear performance based on interaction patterns. The paper argues that superlinearity of the "response field" is often driven by systems being close to criticality. The paper then notes that we need to understand what characteristics of software projects are required for them to be close to criticality. The second mechanism offered is that with more developers, it is more likely a group will have a super contributor who drives the superlinear performance. The paper then unifies these mechanisms using a Hawkes process model. The author notes that in their data they observe hyper productive teams who are even more productive than their model predicts. The paper then relates their findings to superlinear productivity in cities. The paper also concludes that there is a limit to the benefit from increased group size coming from communication complexity.

Sosa and Gero (2012): "Brainstorming in Solitude and Teams: A Computational Study of Group Influence" pdf
Sutton et al. (2010): "The psychology of memory, extended cognition, and socially distributed remembering" pdf
*Tausczik et al. (2014): "Collaborative Problem Solving: A Study of MathOverflow" pdf
Theiner et al. (2010): "Recognizing group cognition" pdf
Voiklis et al. (2006): "An Emergentist Account of Collective Cognition in Collaborative Problem Solving" pdf
West (2007): "Collective Cognition: When Entrepreneurial Teams, Not Individuals, Make Decisions" link
Wooldridge and Jennings (1994): "Formalizing the Cooperative Problem Solving Process" pdf

Collective Decision-Making

Camps et al. (2013): "Social choice rules driven by propositional logic" link
Conradt and List (2009): "Group decisions in humans and animals: a survey" pdf
Flocken et al. (2012): "The Role of Uninformed Individuals in Making the Right Group Decisions" pdf
Sayama et al. (2013): "Evolutionary perspectives on collective decision making: Studying the implications of diversity and social network structure with agent-based simulations" link

Collective Problem-Solving

Bavelas (1950): "Communication Patterns in Task‐Oriented Groups" link
Kearns et al. (2006): "An Experimental Study of the Coloring Problem on Human Subject Networks" link
Mason et al. (2005): "Propagation of Innovations in Networked Groups" pdf

Collective Sensing

Garcia-Herranz et al. (2014): "Using Friends as Sensors to Detect Global-Scale Contagious Outbreaks" link

Consensus and Negotiation

Belesiotis et al. (2010): "Agreeing on Plans Through Iterated Disputes" pdf
Barbara J. Grosz et al. (2000): "The influence of social norms and social consciousness on intention reconciliation" link
Estrada and Vargas-Estrada (2013): "How Peer Pressure Shapes Consensus, Leadership, and Innovations in Social Groups" link
Hazon et al. (2013): "How to Change a Group’s Collective Decision?" pdf

Cooperation

Anh Han et al. (2013): "Good Agreements Make Good Friends" pdf
Capraro et al. (2014): "Cooperation increases with the benefit-to-cost ratio in one-shot Prisoner's Dilemma experiments" link
Devaine et al. (2014): "Theory of Mind: Did Evolution Fool Us?" link
Glass and Grosz (2003): "Socially Conscious Decision-Making" pdf
Mason et al. (2014): "Long-run Learning in Games of Cooperation" pdf
Rand et al. (2014): "Social heuristics shape intuitive cooperation" pdf
Tayler et al. (2010): " When Should There be a “Me” in “Team”?Distributed Multi-Agent Optimization Under Uncertainty" pdf (In which is it argued that cooperation can sometimes harm collective performance)
Wang et al. (2013): "Impact of Social Punishment on Cooperative Behavior in Complex Networks" link

Crowdsourcing

Kittur et al. (2013): "The future of crowd work" link
summary
This paper draws an analogy between crowdsourcing and distributed computation, to some extend building on the coordination framework of Malone and Crowston. The paper incorporates expert requester opinions as well as insights from experienced Turkers on various aspects of crowdsourcing design. The paper also visits how to combine human computation, AI, and systems design. The focus is how to move crowdsourcing beyond simple embarassingly parallel architectures. One interesting idea is to better match workers to tasks through recommendation systems rather than first come first serve, especially since it can be given time-consuming for workers to find tasks. Another interesting idea is to improve the opportunities for promotion and social mobility of crowd workers. A final interesting take-away from this paper is crowdsourcing platforms as platforms for testing ideas from management science. The paper also functions as a well-organized literature review of a large body of academic crowdsourcing research.

Rzeszotarski and Morris (2014): "Estimating the Social Costs of Friendsourcing" pdf
summary
This paper presents an interesting methodology for evaluating the perceived social cost of asking questions to followers on Twitter. The researchers pay participants a bonus and give them a set of questions that they are forced to ask either on a crowdsourcing platform or to their followers. However, if the participants choose to crowdsource rather than ask their followers, a certain amount is deducted from their bonus. The researchers find that higher costs to crowdsourcing increase the amount of asking followers, and some participants chose to never ask followers. At the same time, some participants chose to ask friends rather than crowdsource even with just a 5 cent cost. Moreover, participants who ask some questions to followers are more likely to do so again in the future, and younger participants were more likely to ask followers. It appears that some people perceive asking their friends as costly while others do not.

Experimental Frameworks

*Grosz et al. (2004): "The Influence of Social Dependencies on Decision-Making: Initial Investigations with a New Game" pdf

Flocking and Collective Motion

Boos et al. (2014): "Leadership in Moving Human Groups" link
Christodoulidi et al. (2013): "Phase Transitions in Models of Bird Flocking" link
Gokce (2009): "To flock or not to flock: The pros and cons of flocking in long-range “migration” of mobile robot swarms" pdf
Silverberg et al. (2013): "Collective Motion of Moshers at Heavy Metal Concerts" link

Foraging

Bhattacharya and Vicsek (2013): "Collective foraging in heterogeneous landscapes" pdf
Giraldeau and Caraco (2000): "Social Foraging Theory" link
*Hillel et al. (2013): "Distributed Exploration in Multi-Armed Bandits" link
Reverdy et al. (2014): "Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits" link
Srivastava et al. (2013): "On Optimal Foraging and Multi-armed Bandits" pdf

Human-Computer Interaction

Robert E. Kraut (2003): "Applying Social Psychological Theory To The Problems Of Work" pdf

Leaders

Yu et al. (2010): "Collective Decision-Making in Multi-Agent Systems by Implicit Leadership" pdf

Market Makers

Aseem Brahma et al. (2012): "A Bayesian Market Maker" pdf
Sanmay Das and Malik Magdon-Ismail (2008): "Adapting to a Market Shock: Optimal Sequential Market-Making" pdf

Online Communities

Dedeo (2013): "Collective Phenomena and Non-Finite State Computation in a Human Social System" link Ling et al. (2006): "Using Social Psychology to Motivate Contributions to Online Communities" link
Shuangling Luo et al. (2009): "TOWARD COLLECTIVE INTELLIGENCE OF ONLINE COMMUNITIES: A PRIMITIVE CONCEPTUAL MODEL" pdf
Daqing Zhang et al. (2011): "The Emergence of Social and Community Intelligence" pdf

Organizational Learning

Cohen and Levinthal (1989): "Innovation and Learning: The Two Faces of R & D" link
Cohen and Levinthal (1990): "Absorptive Capacity: A New Perspective on Learning and Innovation" pdf
Levinthal (1997): "Adaptation on rugged landscapes" link
Levinthal and March (1981): "A model of adaptive organizational search" link
Levinthal and March (1993): "The myopia of learning" link

Philosophy

Bratman (1992): "Shared Cooperative Activity" pdf
Grosz and Hunsberger (2006): "The Dynamics of Intention in Collaborative Activity" pdf
Grosz and Kraus (1996): "Collaborative Plans for Complex Group Action" pdf Francis Heylighen (2011): "Self-organization of complex, intelligent systems: an action ontology for transdisciplinary integration" pdf
Searle (1990): "Collective Intentions and Actions" pdf
SEP (2001): "Social Epistemology" link
Tollefson and Dale (2011): "Naturalizing joint action: A processbased approach" pdf

Prediction Markets

Othman and Sandholm (2010): "When Do Markets with Simple Agents Fail?" pdf

Probability Collectives

Huang et al. (2005): "A Comparative Study of Probability Collectives Based Multi-agent Systems and Genetic Algorithms" pdf
Tumer and Wolpert (2004): "A Survey of Collectives" pdf

Shared Intentionality

Angus and Newton (2015): "Emergence of Shared Intentionality Is Coupled to the Advance of Cumulative Culture" pdf
summary
This paper gives a multi-level selection model of gene-culture co-evolution that a population with the ability to share intentionality (in the senses of being able to select strategies together as a pair) can allow faster innovation of technologies with strategic complements.

Small Group Research

Kerr and Tindale (2004): "Group Performance and Decision-making" pdf
Woolley et al. (2008): Bringing in the Experts: How Team Composition and Collaborative Planning Jointly Shape Analytic Effectiveness" link

Social Learning, Belief Dynamics, and Information Cascades

Acemoglu et al. (2009): "Spread of Misinformation in Social Networks" link
Acemoglu and Ozdaglar (2011): "Opinion Dynamics and Learning in Social Networks" link
Altshuler et al. (2013): "Trends Prediction Using Social Diffusion Models" pdf (In which virality is predicted based on a model)
Anderson (2007): "Payoff Effects in Information Cascade Experiments" link
Anderson and Holt (1997): "Information Cascades in the Laboratory" pdf
Anderson and Holt (2008): "Information Cascades Experiments" pdf
Banerjee (1992): "A Simple Model of Herd Behavior" pdf
*Beheim et al. (2014): "Strategic Social Learning and the Population Dynamics of Human Behavior: The Game of Go" pdf
summary
This paper takes the perspective that human history can be modeled through a process similar to Darwinian evolution, and relates this process to social learning. The paper takes as a case study the adoption patterns of opening Go moves in expert Go players. The paper looks at individual-level adoption data, showing that players tend to use opening moves that already know, especially if those opening moves tend to lead to winning games. Interestingly, the authors also provide evidence that whether an opening move tends to lead to wins for other players is more important for individual adoption than personal success with that strategy. Thus, opening moves tend to be more likely to be adopted if they lead to individual success, success of other players in the community, and if they are already somewhat popular.

Benabou (2013): "Groupthink: Collective delusions in organizations and markets" pdf
*Bikhchandani et al. (1992): "A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades" pdf
Bikhchandani et al. (1998): "Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades" link
*Bikhchandani et al. (2007): "Information Cascades and Rational Herding: An Annotated Bibliography and Resource Reference" link
Bond et al. (2012): "A 61-million-person experiment in social influence and political mobilization" link
*Bosse et al. (2013): "Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions" pdf
*Boyd et al. (2011): "The cultural niche: Why social learning is essential for human adaptation" link
*Callander and Horner (2005): "The Wisdom of the Minority" pdf
Celen et al. (2010): "An Experimental Test of Advice and Social Learning" pdf
Celen and Kariv (2003): "Distinguishing Informational Cascades from Herd Behavior in the Laboratory" pdf
Celen and Kariv (2003): "Observational Learning Under Imperfect Information" pdf
Celen and Kariv (2004): "An Experimental Test of Observational Learning Under Imperfect Information" pdf
Choi (2006): "A Cognitive Hierarchy Model of Learning in Networks" pdf
Choi et al. (2004): "Behavioral Aspects of Learning in Social Networks: An Experimental Study" pdf
Choi et al. (2007): "Consistency and Heterogeneity of Individual Behavior under Uncertainty" pdf
Choi et al. (2012): "Social Learning in Networks: A Quantal Response Equilibrium Analysis of Experimental Data" pdf
De Vany and Lee (????): "Information Cascades in Multi-Agent Models" pdf
Eger (2013): "(Failure of the) Wisdom of the crowds in an endogenous opinion dynamics model with multiply biased agents" link
*Eyster and Rabin (2009): "Rational and Naive Herding" pdf
Gale and Kariv (2003): "Bayesian Learning in Social Networks" pdf
Goff and Soulier (2014): "Of ants and urns: estimation of the parameters of a reinforced random walk and application to ants behavior" pdf
summary
This paper suggests reinforced random walks as a model of social learning and provides two ways to estimate the parameters of that model. The authors apply this model to ant colony data from a collective decision-making experiment. Although the paper isn't framed this way, it points to a technique for performing inference in agent-based models.

*Goeree et al. (2007): "Self-correcting Information Cascades" pdf
Goldstone et al. (2013): "Learning Along With Others" link
*Golub and Jackson (2009): "Naive Learning in Social Networks and the Wisdom of Crowds" pdf
*Hamdi and Krishnamurthy (2013): "Removal of Data Incest in Multi-agent Social Learning in Social Networks" link
Hanson and Putler (1996): "Hits and misses: Herd behavior and online product popularity" link
summary
This paper is one of the first to experimentally manipulate online popularity scores. They investigate how experimentally manipulating the download counts of one of each of a set of matched pairs of software files affects the future downloads of those software files. Files were matched if they had similar functionality, were of similar size, had similarly detailed descriptions, had similarly evocative titles, were close to each other in the library list, and had similar popularity already. The authors then increased the download count of the treatment files until they exceedded the control file's popularity by a fixed amount. The authors then recorded the final download counts a day and a half later and for each day the following week. The paper shows huge and prolonged effects, as much as 50% increase in download rate for their highest power intervention for the entire week of observation.

HELIOS HERRERA AND JOHANNES HÖRNER (2012): "A NECESSARY AND SUFFICIENT CONDITION FOR INFORMATION CASCADES" pdf
Hirshleifer and Teoh (2001): "Herd Behavior and Cascading in Capital Markets: A Review and Synthesis" pdf
*Homer-Dixon (2013): "A Complex Systems Approach to the Study of Ideology: Cognitive-Affective Structures and the Dynamics of Belief Systems" pdf
Huang and Chen (2006): "Herding in Online Product Choice" link
summary
This paper provides evidence for a few hypotheses: that higher sales volume will create more sales, that more positive reviews create more sales, that online community recommendations are more influential than expert recommendations, and that online community recommendations are perceived as more trustworthy but less expert than expert recommendations. The authors used laboratory experiments involving presenting participants with different sales figures for the same compared items.

Kariv (2005): "Overconfidence and Informational Cascades" pdf
Kempe et al. (2013): "Selection and Influence in Cultural Dynamics" link
*Kleinberg (2013): "Cascading behavior in social and economic networks" link
Kubler and Weizsacker (2004): "Limited Depth of Reasoning and Failure of Cascade Formation in the Laboratory" link
Kuran and Sunstein (2007): "Availability Cascades and Risk Regulation" pdf
Lamberson (2009): "Social Learning in Social Networks" link
*Mobius and Rosenblat (2014): "Social Learning in Economics" link
summary
This paper reviews the theoretical and empirical economics literature on social learning. The paper contains a lot of pointers to recent evidence for social learning in natural settings. Somewhat surprisingly, the paper does not cite any of the recent high-profile social learning experiments (Fowler, Watts, Taylor). The paper claims that there is a large divide between theoretical and empirical studies of social learning.

Montes de Oca and Stutzle (2008): "Towards Incremental Social Learning in Optimization and Multiagent Systems" pdf
Moretti (2011): "Social Learning and Peer Effects in Consumption: Evidence from Movie Sales" pdf
Mossel (2012): "Asymptotic Learning on Bayesian Social Networks" pdf
Mueller-Frank and Neri (2013): "Social Learning in Networks: Theory and Experiments" link
Noble and Franks (2003): "Social Learning in Multi-agent Systems" pdf
Oberhammer and Stiehler (2001): "Does Cascade Behavior in Information Cascades Re‡ect Bayesian Updating?" pdf
Pagagelis et al. (2009): "Information Cascades in the Blogosphere: A Look Behind the Curtain" pdf
Park et al. (2014): "Modelling the effects of subjective and objective decision making in scientific peer review" link
Pollock et al. (2008): "MARKET WATCH: INFORMATION AND AVAILABILITY CASCADES AMONG THE MEDIA AND INVESTORS IN THE U.S. IPO MARKET" pdf
Raafat et al. (2009): "Herding in Humans" pdf
summary
A nice high-level overview of a lot of herding literature. Includes a list of historically important papers and a conceptual breakdown of the modeling space.

*Rahwan et al. (2014): "Analytical reasoning task reveals limits of social learning in networks" pdf
summary
This paper uses the cognitive reflection test to study propogation of reasoning strategies through social networks via social learning. They connect participants in various network topologies and examine whether over multiple rounds, observing (only) the answers of neighbors improves accuracy within each question (social learning of correct answers) or between questions (social learning of reasoning strategies). Their experiments yielded evidence for contagion of correct responses but did not yield evidence for contagion of correct strategy (reflecting on the questions instead of going with intuition).

Rendell et al. (2011): "Cognitive culture: theoretical and empirical insights into social learning strategies" pdf
Sadikov et al. (2011): "Correcting for missing data in information cascades" link
Salehi (2009): "Complex Networks: New Models and Distributed Algorithms" link
*Salganik and Watts (2008): "Leading the Herd Astray: An Experimental Study of Self-fulfilling Prophecies in an Artificial Cultural Market" pdf
Sharpanskykh (2013): "Modelling and Analysis of Social Contagion Processes with Dynamic Networks" link
Shrethsa and Moore (2013): "A message-passing approach for threshold models of behavior in networks" link
Smith and Sorenson (2000): "Pathological Outcomes of Observational Learning" pdf
Smith and Sorenson (2008): "Rational Social Learning with Random Sampling" pdf
Srivastava and Leonard (2013): "On the Speed-Accuracy Tradeoff in Collective Decision Making" pdf
Torney et al. (2014): "Social information use and the evolution of unresponsiveness in collective systems" link
summary
This paper makes an ambitious claim that collective systems tend to evolve to non-optimal regions of social dependence. In these regions, collectives become non-responsive to changes in their environments because of lock-in. This paper thus formalizes an information cascade-like effect for dynamic environments, which is great. The paper uses optimal decision theory, numerical simulation, and evolutionary analysis to provide support for this claim. However, the results appear to rely heavily on the assumptions that individuals are maximizers and have naive representations of the beliefs of other individuals. The paper predicts that bacterial colonies can be shown in the laboratory to tend to evolve to be overly reliant on social information and more collectively unresponsive to environmental dynamics.

Toyokawa et al. (2014): "Human Collective Intelligence under Dual Exploration-Exploitation Dilemmas" link
Van Dyke Parunak (2009): "A Mathematical Analysis of Collective Cognitive Convergence" pdf
Varshney (2013): "Bounded Confidence Opinion Dynamics in a Social Network of Bayesian Decision Makers" link
Watts (2001): "A simple model of global cascades on random networks" link
Weizsacker (2008): "Do we follow others when we should? A simple test of rational expectations" pdf
*Wisdom et al. (2013): "Social Learning Strategies in Networked Groups" link
Ziegelmeyer et al. (2010): "Fragility of information cascades: an experimental study using elicited beliefs" link

Stigmergy

Sanmay Das and Malik Magdon-Ismail (2010): "Collective Wisdom: Information Growth in Wikis and Blogs" pdf

Swarm Intelligence

*Krause et al. (2009): "Swarm intelligence in animals and humans" pdf
Werfel et al. (2014): "Designing Collective Behavior in a Termite-Inspired Robot Construction Team" link

Team Mental Models

Espinosa et al. (2002): "Shared Mental Models, Familiarity and Coordination: A Multi-Method Study of Distributed Software Teams " pdf
Jonker et al. (2010): "Shared mental models: a conceptual analysis" link
M. Birna van Riemsdijk (2010): "Formalizing organizational constraints: a semantic approach" link

Teams

Garbers and Konradt (2013): "The effect of financial incentives on performance: A quantitative review of individual and team-based financial incentives" link
Hassall (2009): "The Relationship between Communication and Team Performance" pdf
Ilgen et al. (2005): "Teams in Organizations: From Input-Process-Output Models to IMOI Models" link
Rogelberg (2007): "Input–Process–Output Model of Team Effectiveness" link

Theory

*Feinerman et al. (2012): "Collaborative Search on the Plane without Communication" pdf
Shamir (2014): "Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation" pdf
David H. Wolpert (2003): "Collective Intelligence" pdf
David H. Wolpert (2003): "Theory of Collective Intelligence" pdf
David Wolpert and John Lawson (2002): "Designing agent collectives for systems with markovian dynamics" link
David Wolpert and Kagan Tumer (2008): "An Introduction to Collective Intelligence" pdf

Transactive Memory

Argote and Ren (2012): "Transactive Memory Systems: A Micro Foundation of Dynamic Capabilities" pdf
Wegner (1986): "Transactive Memory" pdf

Wisdom of Crowds

Conradt (2011): "Collective behaviour: When it pays to share decisions" link
Galton (1907): "Vox Populi" pdf
Hong and Page (2011): "Some Microfoundations of Collective Wisdom" pdf
Hong et al. (2011): "Individual Learning and Collective Intelligence" pdf
Kao and Couzin (2014): "Decision accuracy in complex environments is often maximized by small group sizes" link
summary
This paper provides conditions in two simple models under which the wisdom of crowds occurs. In the first model, groups achieve the wisdom of crowds if most individuals observe independent information and that information is reliable, as opposed to observing correlated or uncorrelated but unreliable information. The second model considers a more realistic structure of correlated signals and finds similar results.

Koriat (2012): "When Are Two Heads Better than One and Why?" link
summary
Confidence correlates with consensus, not with correctness, even when you don't know what other people have chosen. The paper explores the implications of this result for wisdom of crowds aggregation mechanisms that rely on confidence-weighting. The paper also shows that a simple confidence-weighting model can account for the results of Bahrami et al. even in absence of communication between participants. It's also interesting to consider the implications of this result more generally.

Aniket Kittur and Robert E. Kraut (2008): "Harnessing the Wisdom of Crowds in Wikipedia: Quality Through Coordination" pdf
List (2008): "Collective Wisdom: Lessons from the Theory of Judgment Aggregation" pdf
Lorenz et al. (2011): "How social influence can undermine the wisdom of crowd effect" link
Peter M Krafft Last modified: Thu Aug 6 00:07:09 EDT 2015