Thomas W. Malone and Mark Klein
Harnessing Collective Intelligence
to Address Global Climate Change
Global climate change, caused by human-generated greenhouse-gas emissions, È
perhaps the most pressing and important problem currently facing humanity. È
also unique by virtue of being a truly systemic problem of vast complexity: Esso
affects every one of us, and is directly affected by every one of our actions. Like
nothing else, the climate crisis calls upon us to engage in effective collective deci-
sion making on a global scale.
At the same time, the spectacular emergence of the Internet and associated
information technology has created unprecedented opportunities for new kinds of
interactions, via email, instant messaging, news groups, chat rooms, blogs, wikis,
podcasts, and the like. As the well-known examples of Wikipedia and Linux illus-
trate, it is now possible to combine the work of thousands of knowledgeable and
interested individuals in ways that were completely impossible a few years ago.
But these technologies have not yet been used to deal effectively with our glob-
al problems. Our societal conversations about controversial topics like global cli-
mate change are often strident and unproductive. And we have no clear way to
converge on well-supported decisions concerning what actions, both grand- E
ground-level, humanity should take to solve these problems.
In this paper we argue that it is now possible to harness computer technology
to facilitate “collective intelligence”—the synergistic and cumulative channeling of
Thomas W. Malone is the Patrick J. McGovern Professor of Management at the MIT
Sloan School of Management and the founding director of the MIT Center for
Collective Intelligence. He was also the founding director of the MIT Center for
Coordination Science and one of the two founding co-directors of the MIT Initiative
on “Inventing the Organizations of the 21st Century”. Professor Malone teaches class-
es on leadership and information technology, and his research focuses on how new
organizations can be designed to take advantage of the possibilities provided by infor-
mation technology.
Mark Klein is a Principal Research Scientist at the MIT Center for Center for
Collective Intelligence, and an Affiliate at the Computer Science and AI Lab as well
the New England Complex Systems Institute. He also co-directs the Robust Open
Multi-Agent Systems (ROMA) research group. His research is aimed at enabling more
effective coordination in distributed systems with humans and/or computer-based
agents.
© 2007 Thomas W. Malone and Mark Klein
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Thomas W. Malone and Mark Klein
the vast human and technical resources now available over the internet—to
address systemic problems like climate change. What is needed, we believe, is a new
kind of web-mediated discussion and decision-making forum.
Our focus is on a possible use of such a system with a particularly high social
return: drawing on the best human and computational resources available to
develop government policies about climate change. We will begin with stories
about how different kinds of people could participate in such a global conversa-
zione. Then we’ll briefly describe some of the technologies that would make such a
conversation possible.
STORIES OF A POSSIBLE FUTURE
Today, governmental policy-making is complex, cumbersome, and slow. Experts
can talk past each other, while experts and policy-makers have unproductive con-
versations. News media summaries are necessarily incomplete. Agreements
between countries are especially difficult. And many intelligent members of the
general public are passionate about these issues (on various sides) but see no way
to contribute other than activism or changes in their own personal consumption.
Now, imagine a time a few years from now, when a new kind of on-line forum
exists to help deal with these problems. Imagine that this forum is called the
Climate Collaboratium. And imagine that it is used around the world, by thou-
sands of people on all sides of the issues—experts, policy analysts, legislators, E
concerned citizens—to collectively develop and debate scenarios to respond to
global climate change.
Here are some stories from that possible future—purely fictional characters in
real organizations, involved in activities that are on the cusp of feasibility:
Future Story 1.A Scientific Expert
As a graduate student in MIT Prof. Susan Lim’s lab, one of Steve McKinnon’s
responsibilities is to report the lab’s new results on the Climate Collaboratorium
website. Today is an especially important day for the lab. Nature magazine has just
published an article from Lim’s lab estimating that the Southern Ocean’s absorp-
tion rate for carbon dioxide is a surprisingly low 0.11 gigatons of carbon per year.
Steve goes to the section of the Collaboratorium website that summarizes the
key arguments about many issues related to global climate change. He finds the
issue called “What is the carbon dioxide absorption rate in the Southern Ocean?"
and adds a new position stating the new result. Then, to support the new position,
he adds an argument that briefly summarizes the Nature article and includes a link
to its on-line version. This information is now easily available to anyone in the
world who cares about this issue.
The positions that people had entered previously for the issue almost all had
higher values than the new position. Most of the positions had been rated by a
group of ocean scientists, including Prof. Lim, and the most highly rated previous
position (endorsed by 39% of the experts voting) was an absorption rate of 0.33
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Harnessing Collective Intelligence to Address Global Climate Change
gigatons of carbon per year. Because of the reputation of Lim’s lab and the preci-
sion of their new measurement technique, Steve is confident that many of these
experts will change their ratings over the next few weeks, and the new position he
has just entered will become the most highly rated one.
Steve could stop there for today, but he is curious how much effect this new
result will have on the overall climate situation. To test this, he goes to another part
of the Collaboratorium that shows complete scenarios for what is likely to happen
under different policy choices and other assumptions. He looks at two widely fol-
lowed scenarios: (1) the “business as usual” scenario maintained by the MIT
Program on the Science and Policy of Global Change reflects what will likely hap-
pen over the next 100 years with no significant changes in current policies and
lifestyles; (2) the “Sierra Club” scenario, maintained by the Sierra Club, reflects a
set of assumptions about aggressive policy and lifestyle changes by people and gov-
ernments around the world.
In both scenarios, Steve goes to the issue for which he just added a new posi-
zione, and indicates that he wants to adopt the new position. This automatically cre-
ates two new scenarios (shown as “children” of the scenarios with which he start-
ed). Then Steve clicks the “Simulate now” button and watches the new results
appear. Even though Steve had worried that the results might have been even
worse, the new simulations both show increases relative to the starting scenarios of
only about .02 degrees Celsius in the global average temperature in 2100, with rel-
atively minor effects on global economic output, adverse weather events (like hur-
ricanes), and estimated quality of life.
Ovviamente, the “Simulate now” option uses many rough approximations in its
calculations. Full-scale simulations, using much more detailed calculations, can
take days to run even on today’s computers. But Steve thinks these new results are
important enough that he clicks the button to “Request full simulation” for both
new scenarios. He is pretty confident that either the MIT center or one of the other
universities with full-scale simulations will be intrigued enough to run his new sce-
narios and generate much more detailed (and believable) predictions that incor-
porate Steve’s new data.
Future Story 2.An Advocate
Sarah Schmitt is the Director of Policy Analysis for the United States Climate
Action Partnership (USCAP), an alliance of businesses and environmental groups
including Boston Scientific, BP, General Electric, General Motors, and Shell.
Starting about 18 months ago, Sarah and her staff led the creation of a set of
detailed scenarios in the Climate Collaboratorium showing the consequences of
USCAP’s proposed program for capping emissions and then letting organizations
buy and sell emission permits among themselves (“cap and trade”). Most of the
people working on these scenarios were not actually employees of USCAP. Some
were employees of USCAP member organizations paid to do this as part of their
job, but many were volunteers—students, retired scientists, environmental hobby-
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Thomas W. Malone and Mark Klein
ist, and Wikipedia contributors—attracted to the USCAP scenarios because they
seemed particularly promising.
The scenarios included detailed scientific assumptions about population
growth, emissions rates for different kinds of vehicles, upper atmosphere chem-
istry, and so forth. But most of the people developing the USCAP scenarios were
not computer programmers or climate change scientists themselves. Invece, Essi
were able to build their scenarios by just selecting combinations of positions that
had already been entered by specialists in different disciplines.
Sarah and her staff were happy to see that most of the simulated results of the
USCAP cap-and-trade program were good under all the plausible combinations of
different scientific assumptions. In fact, after USCAP endorsed these scenarios,
Sopra 80,000 other Collaboratorium users also endorsed them as their preferred
alternatives.
Despite many conversations over the years, the US Chamber of Commerce is
still not a member of USCAP. Infatti, the chamber has tried to represent the inter-
ests of its many small-business members by developing a competing set of scenar-
ios in the Collaboratorium. Today Sarah’s staff has just shown her a new set of sce-
narios which they think incorporate the key elements of both the chamber and the
USCAP scenarios.
After seeing the new scenarios, Sarah decides to recommend to her boss that
USCAP propose them to the chamber. Ovviamente, this will all take some negotiat-
ing. But Sarah is convinced that some form of these new scenarios will be enough
to bring the chamber on board.
And when that happens, the Chamber of Commerce will bring with it the
endorsements of over 50,000 members who have given their Collaboratorium
proxies to the Chamber of Commerce. With these new endorsements, the USCAP
scenarios will become—at least for a while—the most highly ranked scenarios in
the entire Collaboratorium!
Future Story 3. A High School Student
Dinesh Rao is a high school student in Mumbai, India. He first heard of the
Climate Collaboratorium in his honors science class last year. One of the class’s
homework assignments was to use a simple entry-level simulation in the
Collaboratorium and find a combination of parameters that would reduce the
atmospheric concentration of carbon dioxide to less than 350 parts per million by
the year 2100.
Dinesh’s teacher had also asked the students to study the most highly rated
arguments on both the “yes” and “no” positions for the issue “Should we rely on
unknown future technological advances to solve climate problems that would be
expensive to address today?” Unlike many of the positions in the Collaboratorium,
this one is a philosophical issue, not directly tied to specific parameters or other
simulation elements in particular scenarios. But this issue has attracted a lot of
Attenzione, and the arguments on both sides of the issue are very well developed.
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Harnessing Collective Intelligence to Address Global Climate Change
Dinesh found the climate change work so interesting that this year he joined a
student team in the FIRST Climate Change competition. FIRST is a US-based non-
profit founded to inspire young people’s interest and participation in science and
technology. For years, it has organized competitions where teams of high school
students build robots, and it has recently started a new competition based on the
Collaboratorium.
The goal of this competition is to come up with climate change scenarios that
are both plausible and desirable. To be plausible, a scenario has to use positions
that have probabilities of at least 50% as rated by FIRST-approved experts in the
relevant subjects. To be desirable, a scenario should include the smallest possible
increase in global average temperature, and the highest possible value for global
economic output and estimated quality of life.
Dinesh’s team is working on a detailed scenario that includes massive use of
telework (including telephone, email, and advanced videoconferencing) to replace
most daily commuting and business travel. Dinesh and a couple of his teammates
are excellent computer programmers, so they are writing some new software mod-
ules for the simulation itself. Two other teammates are artists developing a video
enactment of what life would be like for a Mumbai call center worker under this
scenario. Dinesh’s team has already won several awards in the Mumbai competi-
zione, and he thinks they have a good chance of winning more in the Indian nation-
al competition.
Dinesh also knows that last year, many elements from the scenario developed
by a winning high school team in California were later adopted by the
Conservative Party in the U.K., and he dreams that his team’s work may someday
be as influential.
Future Story 4.An Open-Source Software Developer
Matt Shields works in Santa Clara, California, designing integrated circuits for
Cisco. He loved programming in college, but now he doesn’t get much chance to
write software at work. So for several years, he has spent some time at night and on
weekends contributing to the Linux open source software project.
Matt is also passionate about environmental issues. He drives a hybrid car,
recycles everything he can, and recently has been thinking about getting involved
with some kind of environmental activism. When he heard about the Climate
Collaboratorium, he knew it was for him! Here was a place where he could use his
programming skills, indulge his passion for software, and try to make the world
better, all at once.
The current version of the most widely used Collaboratorium simulator
divides world economic activity into five major regions. For the last six months,
Matt has been working on new software modules that will allow people to subdi-
vide these regions into much smaller units (such as countries, stati, and cities).
Matt had a clever idea about how to do this, and he’s now almost through debug-
ging the new software he wrote. With a little luck, he hopes to get his new modules
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Thomas W. Malone and Mark Klein
accepted by the Collaboratorium software committee and included in an official
software release sometime next month. And he hopes that soon after that, people
all over the world will be using his software to develop more detailed models of
their own cities and regions.
One of Matt’s friends recently got a job offer from Google, based in part on the
volunteer programming work he had done on the Collaboratorium. Matt is happy
with his current job, so he’s not looking to copy his friend. But he sometimes day-
dreams about a day—many years from now—when he can tell stories to his grand-
children about how, in his twenties, he helped saved the world from a global cli-
mate catastrophe.
Future Story 5.A congressional staffer
Mary Dominguez is a senior legislative assistant for U.S. Sen. Karen Williams
(Democrat, Colorado). Mary is responsible for all the environmental legislation in
Sen. Williams’ office; for the past couple of years she has been monitoring changes
in the Climate Collaboratorium fairly frequently. She has noticed that shifts in
Collaboratorium endorsements are often good predictors of how public opinion
polls will change several months later.
Recently she noticed a significant increase in endorsements for tighter limits
on a cap-and-trade program in the U.S., so she decides to talk to Sen. Williams
about how this new information should influence the environmental bill the sen-
ator is currently drafting with a Republican colleague.
HOW COULD ALL THIS BE DONE?
The stories you have just read are not impossible fantasies. With a suitable combi-
nation of technological support and on-line community building, all could feasi-
bly be realized. Three primary types of technology would be required: (1) on-line
argumentation systems; (2) computer simulations; E (3) collective decision-
making tools.
On-line Argumentation Systems
Today’s on-line discussion forums, blogs, and chat rooms do a good job of encour-
aging lots of people to express their opinions and share them widely. But these sys-
tems are not very good at supporting evidence-based, logical deliberation: IL
quality of contributions can vary enormously. Discussions of controversial issues
are often hijacked by a narrow set of “hot” issues or “loud” voices. The same basic
ideas may be entered many times in slightly different ways in different places. E
it is difficult to find the most important comments on a specific issue embedded
in many other topics.
One promising approach for dealing with these problems is using systems for
on-line argumentation1. As several of the stories above suggest, these systems can
help groups define networks of issues (questions to be answered), positions (alter-
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Harnessing Collective Intelligence to Address Global Climate Change
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Thomas W. Malone and Mark Klein
native answers for a question), and arguments (statements that support or detract
from some other statement) (See Figure 1).
Such tools can help make deliberations, even complex ones, more systematic
and complete. The structure implicitly encourages users to express the evidence
and logic in favor of the positions they prefer. The results are captured in a com-
pact form that makes it easy to understand what has been discussed to date and, if
desired, add to it without needless duplication; this encourages synergy among
group members and helps the system accumulate information and opinions across
time.
Tools like these have been used successfully for decades in small face-to-face
groups with skilled facilitators who help the groups organize their comments into
a logical structure. But a critical barrier to the use of these systems is the effort and
skill required to structure discussions in this logical way. Fortunately, Tuttavia,
such systems might actually work better at the large scale suggested by the stories
above, for a few reasons.
Primo, the number of distinct issues, options, and arguments in a discussion will
grow much more slowly than the number of participants. Così, after a few of the
key issues, positions, and arguments have been entered, it will become much easi-
er to find the places where additional ones should go. Secondo, in a large, web-based
conversation, people who are naturally good at mapping arguments in this logical
way will be able to apply their skills more easily over a much larger base of users
and comments. Just as some people in Wikipedia spend much of their time copy-
editing contributions others have made, we expect that some people in a forum
like this would spend much of their time improving the logical structure for com-
ments that others have made.
Together, these aspects of structured argumentation systems have the potential
to make group deliberation much more productive than ordinary conversations.
But the users of such systems are still, in a sense, “just talking.” They might, for
instance, spend huge amounts of time analyzing and debating issues that are actu-
ally trivial in the overall scheme of things. And they have no systematic way of
understanding how positions on different issues interact with each other. For
esempio, will the potential benefits of energy-saving light bulbs in US homes be
completely overwhelmed by emissions from coal-powered electric plants in China?
And if people telecommute from home more often, will the reduced carbon emis-
sions from less commuting be outweighed by increased emissions from more
home heating?
The only way to answer questions like these accurately is to use quantitative
models to analyze complete scenarios of the whole climate-related system. E
that requires another type of technology: computer simulations.
Computer Simulations
Fortunately, many organizations have developed computer simulations that make
this possible.2 These simulations include economic and social factors such as pop-
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Harnessing Collective Intelligence to Address Global Climate Change
Figura 2. Sample simulation program (from Senge et al, 2007).
© 2007 Schlumberger Ltd.
ulation levels, transportation usage, residential heating, and manufacturing inten-
sity, as well as physical factors such as carbon emissions, ocean chemistry, and the
effects of atmospheric carbon on temperatures at the earth’s surface.
In order to run these simulation programs, a set of parameters is needed.
Fortunately, as the stories above suggest, the selection of these parameters can be
linked to the argumentation system in a natural way. Each parameter in a simula-
tion can be mapped to an issue in the argumentation system, and the different
positions for that issue can correspond to different possible values for the param-
eter. For instance, in the first story above, Steve McKinnon entered a new value for
the parameter representing an issue about carbon dioxide absorption rate in the
Southern Ocean. Then, after he added the value, both he and others could add
arguments, for and against that position.
By choosing positions for all the parameters in a given simulation, users can
define scenarios. Then, if they want, they can also ask for these scenarios to be sim-
ulated. Simple simulations can be run immediately. If a simulation program
requires substantial computer time, the request may need to be approved and the
results may not be available for days. In either case, the complete scenario, includ-
ing the results of the simulation, can then be stored for others to review.
The easiest way to start developing a system like this is with a single, simple
simulation program, such as the one shown in Figure 2. But as the stories at the
beginning of this article suggest, many alternative simulations—developed by
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Thomas W. Malone and Mark Klein
many different people and organizations—could potentially be linked into the
same framework. And users of the system could then debate the relative advantages
and disadvantages of different simulation programs as they evaluate the overall
scenarios being analyzed using these programs. One possible outcome of this
effort, Perciò, might be a vast library of (somewhat) interoperable and publicly
accessible climate-related simulation programs developed by many people all over
the world.
Collective decision-making tools
On-line argumentation and computer simulation provide a powerful way to
explore many possible models of—and responses to—global climate change. Ma
using these approaches alone runs the risk of just generating and evaluating an
ever-expanding set of possibilities, with no way to converge on the most promis-
ing. For that, a third type of technology is needed: tools for collective decision-
making.
A simple but useful possibility, for instance, is just to let all users of the system
vote on the position(S) they favor for each issue. Then, the system can automati-
cally display positions in order of the votes they have received, and it can automat-
ically run simulations every day showing the combined results of the most highly
ranked positions.
A slightly more sophisticated possibility is to show votes separately for differ-
ent kinds of users. For instance, for issues that involve specific scientific expertise
(like the Southern Ocean’s rate of carbon dioxide absorption), the system might
show only the votes from people who have been certified as having expertise in
those areas. But for issues involving value choices (such as “How much economic
sacrifice should we make today to reduce the probability of substantial sea level
rises for our great-grandchildren?"), the votes of ordinary people might count like
those of experts.
In some cases, it may also be appropriate to use not just votes, but probability
estimates. One promising way to generate probability estimates from a group is to
use “prediction markets” where people buy and sell predictions about uncertain
future events and are paid only if their predictions are correct. Such prediction
markets have been found to be surprisingly accurate in a wide range of situations,
including forecasting product sales and US presidential elections3.
One of the most intriguing possibilities is what might be called “proxy democ-
racy”4. With this approach, rather than expecting everyone to vote on all issues,
users could give their voting proxies to other individuals or groups whenever they
wanted to. For example, you might give your proxy on scientific issues to the
Federation of American Scientists while giving your proxy on “values” issues to the
Sierra Club. Anyone who has someone else’s proxy could, in turn, delegate it fur-
ther, and you could always see how those who had your proxy voted on your
behalf. If you didn’t like the way someone used your proxy on a given issue, you
could always retrieve your proxy and vote directly on that issue.
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Harnessing Collective Intelligence to Address Global Climate Change
Such a system doesn’t require people to spend any more time on issues than
they want to, but it lets them express their opinions at whatever level of detail they
want, from very general to very specific.
As the stories above suggest, proxy democracy also has another desirable prop-
erty: organizations that want to increase their influence are motivated to negotiate
with other groups and to recruit more of their supporters to join the overall dis-
cussion forum because that will increase the number of people whose proxies they
controllo.
CONCLUSION
The web-based forum we have described is, simultaneously, a kind of Wikipedia
for controversial topics, a Sims game for the future of the planet, and an electron-
ic democracy on steroids. If we could build it, our societal conversation about
global warming could go beyond the realm of the all-too-often emotionally-driv-
en yes/no votes about small numbers of simplified alternatives. It could, instead,
facilitate reasoned and evidence-based collective decision-making about highly
complex issues.
Acknowledgements
We gratefully acknowledge the support of this work by the Argosy Foundation on
behalf of John Abele. We are also grateful to Kara Penn, John Sterman, and Iqbal
Quadir for their contributions to the work described here.
Endnotes
1. See Moor and Aakhus (2006); Klein and Cioffi (2007).
2. See for example Nordhaus (1994); Dowlatabadi (1995); Weyant (1999); Fiddaman (2002, 2007);
Sterman (2002).
3. Vedere, Per esempio, Wolfers and Zitzewitz (2006).
4. Vedere, Per esempio, Malone (2004, P. 65, footnote 21) E
http://en.wikipedia.org/wiki/Liquid_democracy.
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