Millennium Global Challenge No. 9. How can the capacity to decide be improved as the nature of work and institutions change?

Reference: The Millennium Project

The increasing complexity of everything for much of the world is forcing humans to rely more and more on computers. In 1997 IBM’s Deep Blue beat the world chess champion. In 2011 IBM’s Watson beat top TV quiz show knowledge champions.What’s next? Just as the autonomic nervous system runs most biological decisionmaking, so too computer systems are increasingly making the day-to-day decisions of civilization. We have far more data, evidence, and computer models to make decisions today, but that also means we have far more information overload and excessive choice proliferation. The number and complexity of choices seem to be growing beyond our abilities to analyze, synthesize, and make decisions. The acceleration of change reduces the time from recognition of the need to make a decision to completion of all the steps to make the right decision.

Many of the world’s decisionmaking processes are inefficient, slow, and ill informed. Today’s challenges cannot be addressed by governments, corporations, NGOs, universities, and intergovernmental bodies acting alone; hence, transinstitutional decisionmaking has to be developed, and common platforms have to be created for transinstitutional strategic decisionmaking and implementation. Previous economic models continue to mistakenly assume that human beings are well-informed, rational decisionmakers in spite of research to the contrary. And relying on computer models for decisions proved unreliable in the financial crisis. However, some progress has been made.

Adaptive learning models such as cellular automata, genetic algorithms, and neural networks are growing in capability and accuracy, and databases describing individual behavior are becoming even more massive. In social sciences it has been difficult to develop “laws” to forecast social behavior and, hence, make good decisions based on forecasted consequences. With the advent of massive digital databases and new software, we can let the computer make more empirically based forecasts of the plausible range of how people will react to various decisions. At the same time, increasing democratization and interactive media are involving more people in decisionmaking, which further increases complexity. This can reinforce the principle of subsidiarity—decisions made by the smallest number of people possible at the level closest to the impact of a decision. Fortunately, the world is moving toward ubiquitous computing with institutional and individual collective intelligence (emergent properties from synergies among brains, software, and information) for “just-in-time” knowledge to inform decisions. Ubiquitous computing will increase the number of decisions per day, constantly changing schedules and priorities. Decisionmaking will be increasingly augmented by the integration of sensors embedded in products, in buildings, and in living bodies with a more intelligent Web and with institutional and personal collective intelligence software that helps us receive and respond to feedback for improving decisions.

Cloud computing, knowledge visualization, and a variety of decision support software are increasingly available at falling prices. DSS improves decisions by filtering out bias and providing a more objective assessment of facts and potential options. Some software lets groups select criteria and rate options, some averages people’s bets on future events, while others show how issues have alternative positions and how each is supported or refuted by research.

The MIT Collective Intelligence Center sees its mission as answering “How can people and computers be connected so that collectively they act more intelligently than any individuals, groups, or computers have ever done before?” They are trying to develop measures of collective intelligence (like IQ tests for individuals). Rapid collection and assessment of many judgments via on-line software can support timelier decisionmaking. (See the attached CD Appendix L for an explanation of the Real-Time Delphi.) Google invited “citizen cartographers” to refine the U.S. map. This sort of activity is fundamentally different than the “wisdom of crowds” in which the average judgment is taken to be an answer to unresolved issues. The “wisdom of crowds” approach is essentially a vote, while collective intelligence is a continually emergent property from synergies among data-informationknowledge, software-hardware, and individual and groups of brains that continually learn from feedback. Self-organization of volunteers around the world via Web sites is increasing transparency and creating new forms of decisionmaking. Blogs are increasingly used to support decisions. Issues-based information software in e-government allows decisionmaking to be more transparent and accountable. Although cognitive neuroscience promises to improve decisionmaking, little has been applied for the public.

Political and business decisions include competitive intelligence and analysis to guide decisionmaking; as the world continues to globalize, increasing interdependencies, synergetic intelligence and analysis should also be considered. What synergies are possible among competing businesses, groups, and nations? Synergetic analysis aims to increase “win-win” decisions that assist a larger number of enterprises while reducing the wasted efforts of “win-lose” decisions.

Often decisions are delayed because people don’t know something—a condition Google is beginning to eliminate. Training programs for decisionmakers should bring together research on why irrational decisions are made, lessons of history, futures research methods, forecasting, cognitive science, data reliability, utilization of statistics, conventional decision support methods (e.g., PERT, cost/benefit, etc.), collective intelligence, ethical considerations, goal seeking, risk, the role of leadership, transparency, accountability, participatory decisionmaking with new decision support software, e-government, ways to identify and better an organization’s improvement system, prioritization processes, and collaborative decisionmaking with different institutions.

Challenge 9 will be addressed seriously when the State of the Future Index or similar systems are used regularly in decisionmaking, when national corporate law is modified to recognize transinstitutional organizations, and when at least 50 countries require elected officials to be trained in decisionmaking.

Regional Considerations

Africa: North African revolutions promise to open the decisionmaking processes, increasing freedom of the press to better inform the public. For tribally oriented Africa, the question remains, how can the cultural advantages of extended families be kept while making political and economic decisions more objective and less corrupt? Development of African civil society may need external pressure for freedom of the press, accountability, and transparency of government. Microsoft is collaborating to help e-government systems improve transparency and decisionmaking. If the brain drain cannot be reversed, expatriates should be connected to the development processes back home through Internet systems.

Asia and Oceania: In general, decisions tend to focus more on the good of the family than on the good of the individual in Asian societies; will individualistic Internet change this philosophy? Synergies of Asian spirituality and collectivist culture with more linear, continuous, and individualistic western decisionmaking systems could produce new decisionmaking philosophies. Kuwait is introducing a collective intelligence system and a national SOFI for the Early Warning System in the Prime Minister’s Office. ASEAN could be the key institution to help improve decisionmaking systems in the region.

Europe: Bureaucratic complexity, lack of transparency, and proliferation of decision heads threatens clear decisionmaking in the EU. Europe is experiencing “reporting fatigue” due to so many treaties and bureaucratic rules. Tensions between the EU and its member governments and among ethnic groups are making decisionmaking difficult. Russia is improving policy decisionmaking efficiency by coordination among stakeholders in nanotechnology research among several Councils, Commissions at the Russian Parliament, government, and the Russian Academy of Science. It was a response to the crosssectoral and multidisciplinary nature of nanotech.

Latin America: Chile is pioneering e-government systems that can be models for other countries in the region. For e-government to increase transparency, reduce corruption, and improve decisions, Internet access beyond the wealthiest 20% is necessary. The remaining 80% receive inefficient service, difficult access locations, restricted operating hours, and nontransparent processes. Government institutional design, management, and data for decisionmaking are weak in the region. Latin America has to improve citizen participation and public education for political awareness.

North America: Blogs and self-organizing groups on the Internet are becoming de facto decisionmakers in North America, with decisions made at the lowest level appropriate to the problem. Approximately 20% of U.S. corporations use decision support systems to select criteria, rate options, or show how issues have alternative business positions and how each is supported or refuted by research. Intellipedia provides open source intelligence to improve decisionmaking. The region’s dependence on computer-augmented decisionmaking—from e-government to tele-business—creates new vulnerabilities to manipulation by organized crime, corruption, and cyber-terrorism, as discussed in Challenges 6 and 12.

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