This link will take you to the Google Doc used to create the chapter. Below is a copy lifted from the link on February 26, 2025.
Draft chapter for Thomas W. Malone & Michael S. Bernstein (Eds.) Collective Intelligence Handbook [tentative title], MIT Press, in press.
Chapter 1. Introduction
Thomas W. Malone and Michael S. Bernstein
In nine hours, a team successfully scoured the entire United States to find a set of red balloons worth forty thousand dollars (Pickard et al., 2011). In three weeks, citizen scientists playing a game uncovered the structure of an enzyme that had eluded scientists for over fifteen years (Khatib et al. 2011). In ten years, millions of people authored the most expansive encyclopedia in human history. If interconnected people and computers can accomplish these goals in hours, days, and years, what might be possible in the next years or decade?
This book takes the perspective that intelligence is not just something that arises inside individual brains--it also arises in groups of individuals. We call this collective intelligence: groups of individuals acting collectively in ways that seem intelligent (Malone, Laubacher, & Dellarocas, 2009). By this definition, collective intelligence has existed for a very long time. Families, armies, countries, and companies have all--at least sometimes--acted collectively in ways that seem intelligent. And researchers in many fields--from economics to political science to psychology--have studied these different forms of collective intelligence.
But in the last decade or so a new kind of collective intelligence has emerged: interconnected groups of people and computers, collectively doing intelligent things. For example, Google technology harvests knowledge generated by millions of people creating and linking web pages and then uses this knowledge to answer queries in ways that often seem amazingly intelligent. In Wikipedia, thousands of people around the world have collectively created a very large and high quality intellectual product with almost no centralized control, and almost all as volunteers! And in more and more domains, surprisingly large groups of people and computers are doing tasks from writing software (Lakhani, Garvin, & Lonstein, 2010; Benkler, 2002), to solving engineering problems (Lakhani & Lonstein, 2011), to composing and editing documents (Kittur, Smus, Khamkar, & Kraut, 2011; Bernstein et al, 2010), to predicting Presidential elections (Berg, Forsythe, Nelson, & Rietz, 2008).
We believe these early examples are not the end of the story, but just the beginning. And in order to understand the possibilities and constraints of these new kinds of intelligence, we need a new interdisciplinary field. Such a field can help exploit the--often unrecognized--synergies among different disciplines that have studied various forms of collective intelligence without realizing their commonalities. And it can develop new knowledge that is specifically focused on understanding and creating these new kinds of intelligence. Helping to form such a field is the primary goal of this volume.
What is collective intelligence?
As with many important--but evocative--terms, there have been almost as many definitions of collective intelligence as there have been writers who described it (see Appendix for a representative list). For instance, Hiltz and Turoff (1978) define collective intelligence as “a collective decision capability [that is] at least as good as or better than any single member of the group.” Smith (1994) defines it as “a group of human beings [carrying] out a task as if the group, itself, were a coherent, intelligent organism working with one mind, rather than a collection of independent agents.” And Levy (1995) defines it as “a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills.”
Of course, intelligence itself can be defined in many different ways. For instance, it is sometimes defined in terms of specific processes, such as: “Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience” (Gottfredson, 1997). Another common way of defining intelligence is in terms of goals and the environment, such as: “the ability to adapt effectively to the environment” (Encyclopedia Britannica, 2006), or “the ability to solve problems, or to create products, that are valued within one or more cultural settings (Gardner, 1993). The most common operational definition of intelligence in psychology is as a statistical factor which measures a person’s ability to perform well on a wide range of very different cognitive tasks (Spearman, 1904). This factor, often called “general intelligence” or “g,” is essentially what is measured by IQ tests. There is even controversy about whether it would be legitimate to call behavior intelligent, no matter how intelligent it seemed, if it were done by a computer rather than a person (e.g., Searle, 1999).
In view of all this complexity, our definition of collective intelligence, as given above, is a simple one: groups of individuals acting collectively in ways that seem intelligent. Several aspects of this definition are noteworthy:
(1) The definition does not try to define “intelligence” since there are so many ways to define it, and we do not want to prematurely constrain what we believe to be an emerging area of study. Our definition is, therefore, compatible with all of the above definitions of intelligence.
(2) By using the word “acting”, the definition requires intelligence to be manifested in some kind of behavior. By this definition, for instance, the knowledge represented in a collection like Wikipedia would not, itself, be considered intelligent, but the group of people who created the collection could be.
(3) The definition requires that, in order to analyze something as collective intelligence, one must identify some group of individuals that are involved. In some cases, this may be straightforward, such as noting the individual humans in an organization, but in other cases, it may be useful to draw these boundaries in unusual ways. For instance, one could analyze the operation of a single human brain as collective intelligence, if one regards the whole brain as a group of individual neurons or brain regions. Or one could analyze the collective intelligence of a whole economy by noting that the economy is a collection of many different organizations and people.
(4) The definition requires that the individuals act collectively, that is, that there be some relationships among their activities. We certainly do not intend this to mean that they must all share the same goals or always cooperate. We merely mean that their activities are not completely independent, that there are some interdependencies among them (e.g., Malone & Crowston, 1994). For instance, different actors in a market buy and sell things to each other, even though they may each have very different individual goals. And different problem solvers in an open innovation community like InnoCentive’s compete to develop the best solutions to a problem.
(5) Finally, by using the word “seem,” the definition makes clear that what is considered intelligent depends on the perspective of the observer. For instance, to evaluate whether an entity is acting intelligently an observer may need to make assumptions about what the entity’s goals are. IQ tests, for example, do not measure intelligence well if the goal of the person taking the test is to annoy the person giving the test! Or an observer may choose to analyze how intelligently a group of Twitter users filters information even if none of the individual users have that goal.
How does collective intelligence relate to other fields?
In establishing an interdisciplinary field like collective intelligence it is useful to indicate how the new field overlaps with and differs from existing fields. In that spirit, therefore, we suggest the following loose guidelines for thinking about how collective intelligence relates to several existing fields.
Computer science. Collective intelligence overlaps with the subset of computer science that involves intelligent behavior by groups of people, computers, or both. For instance, "groups" of one person and one computer (human-computer interaction) are peripherally part of collective intelligence, but combining multiple people and computers to solve problems is central to collective intelligence (e.g., human computation, crowdsourcing, social software, computer-supported cooperative work, groupware, collaboration technology). Similarly, studies of artificial intelligence that don’t focus on how different processing units work together are probably not part of collective intelligence, but studies of how groups of computational agents can exhibit intelligent behavior certainly are.
Cognitive science. Cognitive science focuses on understanding the nature of the human mind including many aspects of mental functioning that may be regarded as part of intelligent behavior (such as perception, language, memory, and reasoning). Collective intelligence overlaps with cognitive science only in places where the intelligent behavior arises from groups of individuals. Most obviously, this occurs with groups of people (e.g., group memory, group problem solving, organizational learning), but, as noted above, studies of how different parts of a single brain interact to produce intelligent behavior can also be part of collective intelligence.
Sociology, political science, economics, social psychology, anthropology, organization theory, law. These disciplines all study the behavior of groups of people. They overlap with collective intelligence only when there is a focus on overall collective behavior that can be regarded as more or less intelligent. For instance, analyzing how individual people's attitudes are determined or how they make economic choices would not be central to collective intelligence. But analyzing how different regulatory mechanisms in markets lead to more or less intelligent behavior by the markets as a whole would be central to collective intelligence. Similarly, analyzing how different organizational designs in a company lead to better or worse performance by the company as a whole would also be central to collective intelligence. And so would analyzing how well democratic governments make decisions and solve problems.
Biology. Collective intelligence overlaps with the parts of biology that focus explicitly on group behavior that can be regarded as intelligent. For instance, studies of beehives and ant colonies sometimes focus on how the individual insects interact to produce overall behavior that is adaptive for the group.
Network science. Collective intelligence focuses on the subset of network science that involves intelligent collective behavior. For instance, simply analyzing how fast news diffuses in networks with different topologies would not be central to collective intelligence because there is no overall intelligent behavior being explicitly analyzed. But if such a study also analyzed how effectively the network as a whole filtered different kinds of news or how the speed of information diffusion affected the speed of problem solving, then it would be central to collective intelligence.
History of collective intelligence as a topic of study
The phrase “collective intelligence” has been used descriptively since at least the 1800’s. For instance, physician Robert Graves (1842, pp. 21-22) used it to describe the accelerating progress of medical knowledge, political philosopher Pumroy (1846, p. 25) used it to describe the people’s sovereignty in government, and Shields (1889, pp. 6-7) used it to describe science as a collective endeavor. By 1906, sociologist Lester Frank Ward used the term in something like its modern sense: “The extent to which [society will evolve] will depend upon the collective intelligence. This is to society what brain power is to the individual.” (Ward, 1906, p. 39).
The earliest scholarly article we have found with “collective intelligence” in the title was by David Wechsler, the psychologist who developed some of the most widely used IQ tests (Wechsler, 1971). This article argues that collective intelligence is more than just collective behavior in that it involves cross-fertilization resulting in something that could not have been produced by individuals. About this same time, computer scientist Doug Engelbart was doing his pioneering work on “augmenting human intellect” with computers, including computational support for team cooperation (Engelbart, 1962, p. 105; Engelbart & English, 1968). Later Engelbart used the phrase “collective IQ” to describe this work and its broader implications (e.g., Engelbart, 1995).
In 1978, Roxanne Hiltz and Murray Turoff used the term “collective intelligence” to describe the goal of the computerized conferencing systems they pioneered (Hiltz & Turoff, 1978). In the 1980’s and 1990’s, the term collective intelligence began to be used more and more to describe phenomena from insect behavior (e.g., Franks, 1989) to groups of mobile robots (Mataric, 1993) to human groups (e.g., Por, 1995; Atlee, 1999; Isaacs, 1999) to electronically mediated human collaboration (e.g., Smith, 1994; Levy, 1994; Heylighen, 1999). As best we can tell, the first two books with the phrase “collective intelligence” in their titles appeared in this period: Smith’s (1994) book focused on computer-supported work groups and Levy’s (1994) influential book focused on the worldwide exchange of ideas in cyberspace.
By the 2000’s, the term “collective intelligence” became even more widely used including some of the publications mentioned later in this volume and many others from computer science to spirituality to business (e.g., Szuba, 2001; Hamilton, 2004; O’Reilly, 2005; Segaran, 2007; Jenkins, 2008; Howe, 2009). Of particular importance to the spread of the concept was a best-selling book on The Wisdom of Crowds (Surowiecki, 2004) and other books for a general audience featuring the concept of collective intelligence (e..g, Tapscott & Williams, 2006; Ridley, 2010).
This period also saw the first academic conferences on collective intelligence (Kowalczyk, 2009; Bastiaens, Baumol, & Kramer, 2010; Malone & von Ahn, 2012) and the first academic research centers focusing specifically on this topic (Canada Research Chair in Collective Intelligence, University of Ottawa; started 2002; Center for Collective Intelligence, MIT, started 2006).
Related concepts
In addition to those who used the specific term “collective intelligence,” there have also been writers in many fields who talked about closely related concepts. For example, psychologists have talked about similar concepts since at least the 1800’s, including crowd psychology (Tarde, 1890), crowd mind (LeBon, 1895; Freud, 1922), and the collective unconscious (Jung, 1934). Sociologist Emile Durkheim used the term collective consciousness (1893) for the shared beliefs and values that lead to group solidarity. Economist Adam Smith (1795) talked about the “invisible hand” controlling allocation of resources in a market. And several writers have talked about forms of collective intelligence on a global scale using terms like world brain (Wells, 1938), planetary mind (Teilhard de Chardin, 1955), and global brain (Russell, 1983; Bloom, 2000).
Many of these and a number of other concepts closely related to collective intelligence will be discussed in later chapters of this volume.
Why is a collective intelligence field timely now?
The past few years have seen a palpable shift in the popularity and maturity of collective intelligence research. As noted above, new forms of collective intelligence enabled by information technology are affecting the daily lives of vast numbers of people across the planet. And disciplines from neuroscience to economics to biology are making fundamental breakthroughs in understanding how groups of individuals can collectively do intelligent things.
But if we do not make an effort to synthesize these insights across fields, we will end up with silos of knowledge, redundant efforts in different academic communities, and lost opportunities for interdisciplinary synergies. We believe this is a critical time for these different fields to come together and begin sharing insights.
This urgency is balanced by pragmatics: the scope of the challenge is large, so we must draw on all the resources at our disposal to tackle them. Several of the authors in this volume have already crossed disciplinary lines for their research. Our goal is to help others do so, too. We hope to provide readers with the tools to know when each disciplinary perspective might be useful and with leads to follow to find out more when appropriate.
Overview of this volume
This book aims to catalyze the field of collective intelligence by laying out a shared set of research challenges and methodological perspectives. It draws together work from computer science, biology, management, economics, and social psychology, among others. Each discipline introduces the reader to its foundational work in collective intelligence, its relevant methods and its most important research questions. It will then present a set of impactful classic works as well as recent research results, giving readers the beginnings of a shared set of references for the field.
The first half of the book covers disciplinary foundations. For example, Jeff Bigham, Michael Bernstein, Eytan Adar, Dan Weld, Mausam, Christopher Lin, and Jonathan Bragg lay out computer science’s interest, via human-computer interaction and artificial intelligence, in building platforms for crowdsourcing, guiding complex crowd tasks, automating workflows, quality control, and creating hybrid artificial intelligence / crowd work. Deborah Gordon introduces a biological sciences perspective on collective intelligence, for example explaining emergent behavior in ant colonies. Andrew Lo focuses on economics, covering topics such as mechanism design, markets, and voting. Anita Woolley, Ishani Aggarwal, and Thomas Malone contribute perspectives from social psychology and organizational theory, introducing ideas about how human groups work and how they can be collectively intelligent. Mark Steyvers and Brent Miller introduce ideas from cognitive psychology and cognitive science including cognitive processes and their instantiation in collectives larger than a single individual. Finally, Yochai Benkler, Aaron Shaw, and Benjamin Mako Hill close with a series of perspectives on collective intelligence from law and other social sciences.
These chapters explain each discipline’s core methods--such as system engineering, controlled experiment, naturalistic observation, proof, and simulation--together with its current core set of research results. The goal, in each case, is to help readers recognize and understand the main methods and goals of the different disciplines.
Accompanying each disciplinary chapter, we include a set of impactful academic articles in collective intelligence related to that discipline. We expect that even though many of these papers will be well-known in their respective disciplines, they will be new to most readers of the book from other disciplines. One goal of these sections is, therefore, to help develop a research canon for collective intelligence.
We envision that the reader will begin by delving deep into the chapters on disciplinary perspectives that are adjacent or far from their own expertise, developing the background to reach into the other foundational research in the recommended readings sections associated with each chapter.
Appendix:
Representative definitions of collective intelligence
A collective decision capability [that is] at least as good as or better than any single member of the group (Hiltz & Turoff, 1978)
A form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills (Levy, 1994)
A group of human beings [carrying] out a task as if the group, itself, were a coherent, intelligent organism working with one mind, rather than a collection of independent agents (Smith, 1994)
The ability of a group to “find more or better solutions than … would be found by its members working individually” (Heylighen, 1999)
Collective intelligence is the intelligence of a collective, which arises from one or more sources (Atlee, 2003)
The general ability of a group to perform a wide variety of tasks (Woolley et al, 2010)
Harnessing the power of a large number of people to solve a difficult problem as a group [which] can solve problems efficiently and offer greater insight and a better answer than any one individual could provide (Financial Times Dictionary, 2013)
The capacity of biological, social, and cognitive systems to evolve towards higher order complexity and harmony (Por, 2013)
A type of shared or group intelligence that emerges from the collaboration and competition of many individuals and appears in consensus decision-making in bacteria, animals, and computer networks (Wikipedia, 2013)
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