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jock.mcclellan@gmail.com 9 years, 10 months ago.
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Browse the below list of possible strategies for advancing CI as an emerging field. Most are ideas implied in the work of participants at the conferences at MIT in 2012 and 2014.
There are reasons to support every one of the strategies. And there are arguments against each. Pick one of the most promising and post your rationale for it’s likelihood of being successful. Why is it more useful than others? Or post a strategy of your own, with your reasons for recommending it. Or comment on any ideas, pro or con.
Post your thoughts as replies to this post, using the form at the bottom of the page.
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Apply ideas from research on CI to improve the collective process of understanding collectively intelligent systems.
Use the tools of CI research to analyze the effectiveness of the community of CI researchers.
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.
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.
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 to rate publications, tools and resources for their 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.
Minimize cognitive-market bubbles by encouraging input from thinkers outside
Foster interpersonal contact for researchers interested in CI, through “meetups,” local events, and more personalized use of Internet tools.
Convene regional conferences frequently.
Collect, explain, and rank potential questions for research on CI.
Match participants’ diverse research skills to tasks, using intelligent human-computer project management systems.
Crowdsource participation in CI research studies.
Establish a pool of researchers who can be tapped for crowdsourced research on CI, combining skills.
Develop intelligent software within which crowds of researchers may collaborate in a well-managed way.
Use AI to improve crowdsourced research by assessing contributions and deciding what tasks go to which contributors.
Continually improve research groups’ social perceptiveness and emotional intelligence.
Use social-interaction sensors and human-computer feedback analysis to highlight areas for improvement for research teams.
Structure human-computer research environments so that “who-knows-what-about-what” is remembered automatically.
Improve the peer review processes for papers, to reduce delays and to increase collaboration.
Apply lessons about forms of leadership that foster CI to the structuring of collaborative research on CI.
Use knowledge about swarming to maximize the ability of small groups of CI thinkers to shift direction of societal evolution.
Apply knowledge about decision making to improve social processes.
Demonstrate the epistemic advantages of participatory and deliberative processes to those of representative democracy.
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, for dialogues among linguistically separated researchers.
Compare the dynamics of CI cross-culturally.
Look what is universal in the dynamics of CI, regardless of culture or setting.
Model best practices for CI with students and colleagues.
Reflect on dissonances between what we espouse and what we do.
Within research teams, reflect together, supportively and candidly, on what is nurturing the group’s CI and what is not.
As groups, don’t just learn; learn to learn.
Beware of the comfort of communicating within your own echo chamber.
Design antidotes to collective cognitive convergence, mixing up differing thinkers at conferences, online, and in meetings.
Reward brokers who promote the exchange of ideas across communities.
As cognitive diversity within research teams increases, increase the management of conflict and the focus on tasks.
De-polarize with respectful reason. Identify common ground.
Test assumptions, especially ones viewed as “obvious.”
Within the CI community, seek the wisdom of crowds without the madness of mobs.
Be as clear as possible about goals when collaborating on research.
Be as clear as is feasible about the over-arching goals of the larger CI research community.
Direct students and subordinates toward research on CI.
Make it easier to grant advanced degrees for collaborative research efforts.
Compile and share data on funding sources for research on CI (without listing recipients).
Create incentives for researchers to collaborate within CI social networks.
Distinguish between direct and indirect coordination of research efforts.
Develop human-computer research coordination software that reduces the overhead costs of organizing and harmonizing efforts.
Apply what is known about environments that encourage cooperation to the design of the CI research environment.
Strengthen CI research communities by rewarding network peers or “buddies” in a way that amplifies peer pressure.
Use knowledge of social identity, social learning, and group boundaries in optimizing productivity of the CI communities.
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 moderately early adopters via localite channels.
- Help journalists to popularize the techniques and benefits of CI.
Analyze the patterns of knowledge diffusion among CI researchers.
Analyze the patterns of knowledge diffusion from CI researchers to the general public.
Develop apps which can help groups be more collectively intelligent by following rules of thumb distilled from research.
Elevate a super-salesman for CI research, as Vannevar Bush was for the establishment of the US National Science Foundation.
Ask the world’s 1,426 billionaires to pledge 0.1% of their $5.4 trillion to a global foundation for CI research to benefit of all, forever.
Engage global thought leaders having broad audiences to explain the centrality of research on CI, and its pay-offs.
Publish possible scenarios for the earth with or without improved CI.
Do “force-field” and similar analyses of the barriers and promoters of R&D on CI.
Promote an R&D effort on CI on the scale of the IPCC or Manhattan projects, given the centrality of CI.
Facilitate access to the most powerful computing technologies for demanding research projects and complex modeling.
Delve the recent, digitized, granular and exhaustive data on peer production of knowledge for grounded theories of CI.
Evaluate diverse disciplines’’ premises, biases, methods, and scopes, and seek ways to wash out inherent errors while retaining insights.
Clarify varying usages of terms about CI, especially to promote precise understanding across disciplines.
Evolve terms within the contemporary CI community that are efficient to use, even at the cost of more difficult learnability for later users.
Distinguish between collective forms of: intelligence, information-sharing, adaptation, prediction, learning, judgment, 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.
Categorize the types of algorithms that turn local behaviors into adaptive group behavior.
Identify forms of CI that maximize capability to adapt to changing environmental realities.
Identify the algorithms governing individual researchers’ local interactions, and assess their effects on the collective effort to understand CI.
As financial markets select good price-finding strategies, sharpen & quicken the research market’s selection of reality-finding strategies.
Ask new questions that may reveal general patterns of group behavior and group evolution.
Seek to bridge the micro and macro levels of observation and to unify them in a consilient framework.
Seek ways to fit together theories of CI, genetic evolution, and cultural evolution.
Refine and test measures of CI, and use them to identify factors affecting CI.
Replicate, test, and question findings within the field.
Compile a list of testable, mid-level propositions about the dynamics of CI.
Explore the use of knowledge of prediction markets to plan promising directions for CI research.
Share a list of research questions, identifying ones easily crowdsourced, and ones requiring new tools.
Beware of errors in mental models of individual and collective behavior.
Encourage efforts to develop models of CI that explain and predict behavior across multiple domains.
Continue incremental research within existing, separate disciplines.
Find ways to combine models of CI to increase their collected accuracy.
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.
Find new ways to incentivize and reward people to collaborate on CI R&D.
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 and quantum computers to mine big data and detect patterns in entangled 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.
Speed up the sharing of which research paths are discovering harvestable terrain, & which are not, to optimize the collective foraging.
From current knowledge of CI, develop fitting philosophies of society, politics, epistemology, and ethics.
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