Whether groups can be supported to make better decisions matters. Extending the work of Irving Janis, James Surowiecki argues that the study of groups is important for two reasons: “First, small groups are ubiquitous in American life, and their decisions are consequential. And second, small groups are different in important ways from groups such as market or betting pools or television audiences." Surowiecki alludes to a different collective from what he examined in The Wisdom of Crowds. This site emphasizes the critical difference lies not in size but in the fact that collaborative intelligence engages contributors who are not anonymous.

Many have tried to analyse creativity, and killed it in dissection. Invention’s revelatory aspects seldom sit still for portraits; its emergent unpredictability doesn’t emerge through analysis. Collaborative intelligence isone manifestation of creativity, rooted in the hypothesis that a group mind provides a Petri dish in which to culture and observe creativity while keeping it alive, to observe creativity in action.

Contemporary thinkers cite the urgent need for a long term environmental sustainability strategy. Central to arguing this case is the work of Professor Paul Ehrlich of the Center for Conservation Biology and the work of a large and growing number of thinkers making a case for urgency. From a technology perspective, Kevin Kelly makes the case in What Technology Wants that the Technium, much like life itself, has its own driving force, propelling its own evolution.

“Collaboration” suffers from its history as a value-laden term, lacking substantive theory, method, or tools. Robert Axelrod’s pioneering study, The Evolution of Cooperation, given today's specification of "cooperation" to refer to tasks where all contributors perform the same role (as in rowing a boat) and "collaboration" to refer to tasks where contributors may perform different roles, should have been titled The Evolution of Collaboration because all of his players bring their unique strategies to the game. Axelrod showed that an argument for efficacy can be constructed without resorting to ethics. Today, broad recognition of the growing impact of global warming, and a range of initiatives toward sustainability, call for better cross-disciplinary knowledge-sharing systems and problem-solving tools. Environmental sustainability demands a method and tools not only to fuse data but to filter and integrate our differing interpretations of data — to recognise patterns and support innovation networks driven by individuals with unique leadership skills, ideas, and priorities. These project leaders need problem-mapping and project visualisation “empty construct” plug-ins to share knowledge and track problem-solving in process, enhancing communication across disciplines.

We need a method to apply our collaborative intelligence to sustainability challenges. Just as we cannot reach a consensus of all scientists that global warming is a fact, or that overpopulation is a threat, or that humanity is fundamentally an ethical species (or not), those premises about collaborative intelligence that remain speculative also deserve serious consideration, whether or not they can be tested or proven.

Traditional problem-solving models
typically assume that the problem-solver can a priori state his goals, define his terms, describe his method, and secure consensus as a prerequisite to start. On environmental issues this is often impossible, as has been dramatised repeatedly in debates about global warming. What is needed is a non-consensus-based model, an approach that shares much in common with the approach of origin of life theorists, who study the origin of life without consensus about how to define life, their object of inquiry.

Five paradigm-shifting ideas of collaborative intelligence
overcome the limitations of the traditional problem-solving model, and develop an alternative model based upon principles of collaborative intelligence, draws principles from evolutionary and developmental theory in the life sciences to lay the groundwork for a theory of collaborative intelligence, founded on five paradigm-shifting, original ideas:

First, we wrongly tend to assume that individuality is the foundation for competition, that collaboration requires the suppression of individuality to an agreed consensus. A paradigm shift is needed to understand that exactly the opposite is true. The argument to support this paradigm shift is well-accepted in evolutionary biology. Evolutionary theorists assert that individual differences make evolutionary adaptation possible. Without differences among replicating organisms, evolution could not generate more fit adaptations. This critical principle from evolutionary theory is the foundation for collaborative intelligence. The central role that individuality plays in driving evolutionary innovation parallels the role that individuals play in creating collaborative ecosystems for effective problem-solving.

Second, the A-PR Hypothesis (Autonomy and Pattern Recognition) is proposed as a single unifying principle that defines both life and intelligence, underpinning collaborative intelligence. Our conventional problem-solving model emphasises “objective analysis of the facts,” which cannot tackle many of the problems we face today. Such problems require systematic data-gathering, as well as collection of diverse interpretations of data, harnessing each expert’s autonomy and unique pattern recognition capacities (A-PR ) to drive the synthesis of diverse views toward an optimal outcome. Collaborative intelligence proposes a systematic method to harness individual capacities for pattern recognition and interpretation in problem-solving.

Third, life’s own designing intelligence, the foundation for collaborative intelligence, is embedded in the Dyadic Model, which characterizes competition and collaboration as complementary, co-equal dynamics in evolution. The traditional view that competition for “survival of the fittest” drives evolutionary adaptation is increasingly being complemented by the more recent view that collaboration is not subordinate, but rather co-equal in promoting evolution’s advance toward improved adaptation. I argue, with supporting evidence from the life sciences, that synthesis can be approached as systematically as analysis, recognising that, as in Nature, the process of synthesis must be flexible to adapt and evolve as new information arrives.

Fourth, Garrett Hardin posited the Tragedy of the Commons (1968): Each agent, pursuing his own self-interest for survival of the fittest, exploits shared resources to everyone’s detriment. Collaborative intelligence addresses the challenge posed by Garrett Hardin’s timely warning: What theory, method, and tools can we use to harness our collaborative intelligence to manage the commons? The thesis proposes an approach that relies on the principle of collaborative autonomy, proposed as a prerequisite for collaborative intelligence, and shows how the dynamics of convergence (rather than consensus) can overcome “the consensus barrier,” succeeding where other methods have failed.

Fifth, the Tripod Learning Model is proposed as a vehicle to derive and teach collaborative intelligence and illustrated using the intersection of the work of Irving Janis, James Lovelock, and Buckminster Fuller.

Collaborative intelligence as a research domain aims not only to improve understanding of how distributed individuals can operate more effectively in cross-disciplinary, problem-solving teams but also to contribute to our understanding of how to use multi-agent systems, collective intelligence and artificial life tools. Biological systems, and the dynamics of evolution, inform our understanding of creativity and cognition, augmented by the theory of complex adaptive systems and simulations of artificial life. Collaborative intelligence has roots in the life sciences, cognitive science, earth systems science, and design science.

This website starts from the premise that that there is need for a clearly articulated discipline of collaborative intelligence. A new method is needed to address cross-disciplinary challenges, such as eco-sustainability. Emphasis on scientific method has led to neglect of design method and the field it informs, collaborative intelligence.

Development of a method and tools to support collaborative intelligence
This challenge requires:

• ways to share, filter, process, and integrate diverse interpretations, since future reality is dependent, not only on data, but on our interpretation of what the data means, patterns recognised, through which we interpret data to enhance collaborative intelligence;

• an effective collaborative problem-solving system with capacity to visualise not only objects but also processes — problem-mapping to visualise the status of problem-solving in process, to track convergent threads, and to position incoming contributions where they fit;

• ways to support collaborative autonomy,while retainingindividual uniqueness and ownership in collaborative knowledge-building, avoiding consensus and maximising the richness and diversity of input (as in living systems “genetic diversity” plays a key role in evolutionary creativity).

Analysis can dissect and interpret the past in order to make projections into the future. Although our data and interpretation of the past may change, the past itself is static. Synthesis constructs the future, adopting a constructivist, proactive stance, making interventions, assessing their impact, attempting to bring order to a set of elements that are themselves shifting through time, even as the process of synthesis progresses. Not only do circumstances and priorities shift, the components subject to synthesis are themselves changing, making synthesis a harder problem to manage than analysis, a problem that requires a more systematic method.

Data fusion has contributed to our capacity to interpret and use data. We need equally systematic methods for cross-disciplinary knowledge fusion for innovation and problem-solving — to “raise the collaborative intelligence” of cross-disciplinary teams.

The premise that synthesis can be as systematic as analysis and can be as rigorously studied as analysis, that design method complements scientific method, calls for recognition of the broad significance of design thinking for innovation. More attention given to synthesis, and to collaborative, as opposed to competitive, models for problem-solving, could drive a shift toward new learning models and innovative curriculum. The discipline of Collaborative Intelligence should sit alongside Collective Intelligence and Negotiation Studies as a means to address complex, cross-disciplinary problems with conflicting priorities, such as eco-sustainability.

A range of disciplines are converging toward collaborative intelligence, e.g.

Jeff Gore: "Cooperation and cheating in microbes" MIT Department of Physics.

Understanding the cooperative and competitive dynamics within and between species is a central challenge in evolutionary biology. Microbial model systems represent a unique opportunity to experimentally test fundamental theories regarding the evolution of cooperative behaviors. In this talk, I will describe recent experiments probing the cooperative growth of yeast in sucrose and the cooperative inactivation of antibiotics by bacteria. In both cases we find that cheater strains?which don't contribute to the public welfare?are able to take advantage of the cooperator strains. However, this ability of cheaters to out-compete cooperators occurs only when cheaters are present at low frequency, thus leading to steady-state coexistence. These microbial experiments provide fresh insight into the evolutionary origin of cooperation. In addition, the challenges of maintaining cooperation in a population may have implications for clinically important microbial behaviors such as antibiotic resistance.

David Rand: "Reward, punishment and the evolution of human cooperation" Harvard. Department of Evolutionary Dynamics.

Cooperation, where one individual incurs a cost to benefit others, is a fundamental aspect of all levels of the natural world as well as human society. Yet cooperation poses a challenge to evolutionary biologists and social scientists: How can the fundamentally selfish process of natural selection favor "altruistic" cooperation, and why are humans, as strategic decision-makers, often willing to help others at a cost to themselves? In my talk, I will explore this question using a combination of evolutionary computer simulations and behavioral experiments with humans involving economic games. I will focus particularly on the role of punishment and reward in discouraging free-riding and fostering cooperation. In the realistic context of repeated interactions where reputation is in play, I show that denial of reward promotes cooperation as effectively as costly punishment. Yet costly punishment is destructive and reduces the payoffs of both players, while denial of reward does not. Thus the use of costly punishment is detrimental to both the individual punisher and to the group as a whole. These results emphasize the importance of developing opportunities for constructive interactions between individuals to help prevent the "tragedy of the commons".

Collaborative Intelligence and the Commons
The core principle underpinning collaborative intelligence, collaborative autonomy, is developed through research on the commons. A classic argument about the urgency to address environmental issues remains timely, the December 1968 publication in Science of Garrett Hardin’s Tragedy of the Commons. In 1979, this paper was named “a citation classic.” It has been cited many more times since. Significant is not only the number of citations, but diverse attempts to dismiss Hardin’s argument, which encapsulates the risks of wholesale capitalism and freedom to reproduce. Hardin’s 1974 article, ‘Living on a lifeboat,’ also attracted substantial opposition.

Although in the ecological domain Garrett Hardin’s seminal journal article in Science is one of the most cited works ever produced, it has not, been cited to support work on collaborative intelligence because the field is so new.

The year after publication of Hardin’s paper, Beryl Crowe published “The Tragedy of the Commons Revisited,” highlighting “that there is a subset of problems, such as population, atomic war, and environmental corruption, for which there are no technical solutions” and “no current political solutions,” that these problems threaten human existence and that, “in passing the technically insoluble problems over to the political and social realm for solution, Hardin made three critical assumptions:

(1) that there exists, or can be developed, a ‘criterion of judgment and system of weighting . . . that will ‘render the incommensurables . . . commensurable’ . . . in real life;

(2) that, possessing this criterion of judgment, ‘coercion can be mutually agreed upon,’ and that the application of coercion to effect a solution to problems will be effective in modern society; and

(3) that the administrative system, supported by the criterion of judgment and access to coercion, can and will protect the commons from further desecration.”

This citation emphasises the conflict among theorists working on sustainability. Collaborative intelligence provides a foundation to develop systems for decision support to analyse risks and tradeoffs, and to design computational environments to support collaborative decision-making for these problems. As literature on sustainability technologies grows exponentially, the sub-domain of decision support for cross-disciplinary, sustainable decision-making remains a hard problem.

Implementing collaborative intelligence relies on digital technologies, linking our ecological commons with the new digital commons, translating principles from one domain to the other.

References

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