What strategies could help nurture CI as an emerging field of inquiry?
Post ideas of your own to add to the list below. Or edit the existing list. Use the “+New” menu button at the top of the screen.
Are there lessons from the history of your own discipline’s development that might be instructive for the development of CI as a field? From your own research on CI, are there findings and principles that could guide the collective effort for the community of researchers in CI in developing a new discipline? What is the evidence that a given strategy will produce the desired effect?
Are there any strategies in the list below that seem especially promising? For what reasons would those strategies be the most fruitful?
Are there any strategies below which, although common, would not be fruitful in the case of CI as a new field? Give your rationale.
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Improve communication between CI researchers, using tools like mailing lists, social media, newsletters, video conferences, and webinars.
Increase research use of social networks for communication, especially science-centered sites like ResearchGate, and MyScienceWork.
Support open science efforts like the Public Library of Science, the Open Science Project, the Center for Open Science, and Frontiers.
Apply ideas from research on CI to improve the collective process of understanding collectively intelligent systems.
Create contests with significant prizes for research on CI.
Seek funding for endowed chairs centered on CI.
Increase funding for young researchers.
Convince funders that understanding CI will reap rewards in addressing countless problems, with a high long-term return on investment.
Increase efforts to persuade private funders to support research in CI, to offset continuing declines in public funding.
Encourage the U.S. NSF to establish a well-funded, over-arching Directorate for Multidisciplinary Research.
Promote crowwdfunding for research on CI, perhaps in conjunction with sites like Indiegogo and Experiment.com.
Cultivate dialogue between those centered on the science of CI systems and those centered on the engineering of better systems.
Develop networking apps that would link diverse CI researchers to one another.
Develop recommender systems rating publications, tools and resources for usefulness in advancing knowledge about CI.
Map the network of all researchers investigating some aspect of CI, and suggest ways to improve the flow of ideas between sub-networks.
Analyze citation indices to reveal patterns of idea-flow, influence, collaboration, isolation, and fecundity.
Find ways to join cognitively diverse researchers in common endeavors.
Foster interpersonal contact through local “meetups” for researchers interested in CI.
Collect and rank questions for research on CI.
Establish a pool of researchers who can be tapped for crowdsourced research on CI.
Develop intelligent software within which crowds of researchers may collaborate in a well-managed way.
Improve the peer review processes for papers, to reduce delays and to increase collaboration.
Apply lessons about forms of leadership fostering CI to structure collaborative research on CI.
Establish a new professional organization centered on CI.
Start special interest groups on CI within disciplinary associations.
Expand the circle of researchers to include more non-English speakers.
Use emerging voice recognition, translation, and expression for dialogues among linguistically separated researchers.
Compare the dynamics of CI cross-culturally.
Model best practices for CI with students and colleagues.
Beware of the comfort of communicating within your own echo chamber.
Reward brokers who promote the exchange of ideas across communities.
Test assumptions, especially ones viewed as “obvious.”
Direct students and subordinates toward research on CI.
Grant advanced degrees for collaborative research efforts.
Compile data on funding for research on CI from specific sources (without listing recipients).
Create incentives for researchers to collaborate within CI social networks.
Strengthen CI research communities by rewarding network peers or “buddies” in a way that amplifies peer pressure.
Publicize usefulness of CI findings for organizations and policy makers.
Seek collaboration with “real-world” actors.
Analyze the economics of social-computer-human configurations to optimize the cost/benefit ratio in the deployment of CI systems.
Apply principles of traditional and behavioral economics to the design of an effective community of CI researchers.
Diffuse lessons about CI first among innovators via cosmopolite channels, and later among early adopters via localite channels.
Help journalists like James Surowiecki to popularize the techniques and benefits of CI.
Develop apps which can help groups be more collectively intelligent by following rules of thumb distilled from research.
Engage global thought leaders having broad audiences to explain the centrality of research on CI and its pay-offs.
Promote an R&D effort on CI on the scale of the Intergovernmental Panel on Climate Change or the Manhattan Project.
Facilitate access to the most powerful computing technologies for demanding research projects and complex modeling.
From current knowledge of CI, develop fitting philosophies of society, politics, epistemology, and ethics.
Clarify varying usages of terms about CI.
Distinguish between collective forms of intelligence, information-sharing, adaptation, prediction, learning, and wisdom.
Identify elements common to CI in all systems – biological, artificial, and human – and establish a common taxonomy.
Identify traits of CI, from the smallest to the largest systems, showing which are held in common and which not.
Seek to bridge the micro and macro levels of observation and to unify them in a consilient framework.
Refine and test measures of CI, and use them to identify factors affecting CI.
Compile a list of testable, mid-level propositions about the dynamics of CI.
Share a list of research questions, including ones not yet easily addressed with existing tools.
Encourage efforts to develop models of CI that explain and predict behavior across multiple domains.
Continue incremental research within existing, separate disciplines.
Develop a meta-lab on CI itself, after the model of the MIT “Climate CoLab.”
Develop a central, impartial online clearinghouse of information about CI.
Promote alternative intellectual property rights arrangements such as the Creative Commons License and the General Public License.
Ease access to governmental, commercial, and telephonic big data for research on CI (in a way that protects privacy and property rights).
Use the speed of access to big data to repeat, replicate, and vary experiments more quickly than by traditional grant processes.
Embrace the purely correlational findings made possible with big data, with less immediate pressure to identify cause.
Encourage open sharing of raw data used in research projects on CI.
Give credit to all collaborators.
Embed principles of CI in education at all levels.
Train people in social skills that improve group intelligence.
Experiment with ways to synergize machine and human intelligences in real time to improve group intelligence.
Apply emerging tools to augment observation of group processes, as with sociometric badges, Google Glasses, and smartphone sensors.
Use smartphone capabilities to speed up interactive surveying and data gathering about CI.
Mine data from the emerging “internet of things.”
Develop ways to use mega-scale computers and quantum computing to mine big data and detect patterns in collective learning systems.
Exploit emerging abilities for automatic semantic-based analysis of natural language mega-data to understand the flow of ideas.
Apply principles of evolutionary programming to improving CI.
Develop tools that simplify researchers’ querying of big data so they can focus on questions rather than on programming.
Democratize the use of the cloud for research, as in the Cloud Research Engagement Initiative using Microsoft’s Azure cloud.