Scalable Cooperation
Reimagining the way society organizes, cooperates, and governs itself.

Over millennia, humans have invented various forms of social organization to govern themselves, ranging from tribes and clubs, to technocracies and democracies. These institutions enabled us to scale up our ability to coordinate, cooperate, exchange information, and make decisions. Today, instant connectivity, online social networks, pervasive algorithms, crowdsourcing, and big data can help us improve our existing cooperative institutions. More significantly, however, these technologies invite us to reimagine completely the ways in which we organize, cooperate, and govern. Our group aims to (1) understand how technology is reshaping the nature of human cooperation; and (2) imagine and design radically new ways of scaling up cooperation.

Research Projects

  • Cognitive Limits of Social Networks

    Iyad Rahwan and Lorenzo Coviello

    There is a wide cultural belief in the power of the Internet and social media as enablers of collective intelligence. They help us spread information rapidly, and learn useful information from each other. But there are fundamental limits to the capabilities of those networks. Understanding these limits is essential to improving social media and allowing society to make the most of it.

  • Crowdsourcing a Manhunt

    Iyad Rahwan, Sohan Dsouza, Alex Rutherford and Manuel Cebrian

    People often say that we live in a small world. In a brilliant experiment, legendary social psychologist Stanley Milgram proved the six degrees of separation hypothesis: that everyone is six or fewer steps away, by way of introduction, from any other person in the world. But how far are we, in terms of time, from anyone on Earth? Our team won the Tag Challenge, a social gaming competition, showing it is possible to find a person, using only his or her mug shot, within 12 hours.

  • Crowdsourcing Under Attack

    Iyad Rahwan and Manuel Cebrian

    The Internet has unleashed the capacity for planetary-scale collective problem solving (also known as crowdsourcing). However, the very openness of crowdsourcing makes it vulnerable to sabotage by rogue or competitive actors. To explore the effect of errors and sabotage on the performance of crowdsourcing, we analyze data from the DARPA Shredder Challenge, a prize competition for exploring methods to reconstruct documents shredded by a variety of paper shredding techniques.

  • Ethics of Autonomous Vehicles

    Iyad Rahwan, Edmond Awad, Sohan Dsouza, Azim Shariff and Jean-François Bonnefon

    Adoption of self-driving, Autonomous Vehicles (AVs) promises to dramatically reduce the number of traffic accidents, but some inevitable accidents will require AVs to choose the lesser of two evils, such as running over a pedestrian on the road or the sidewalk. Defining the algorithms to guide AVs confronted with such moral dilemmas is a challenge, and manufacturers and regulators will need psychologists to apply methods of experimental ethics to these situations.

  • Honest Crowds

    Iyad Rahwan, Lorenzo Coviello, Morgan Frank, Lijun Sun, Manuel Cebrian and NICTA

    The Honest Crowds project addresses shortcomings of traditional survey techniques in the modern information and big data age. Web survey platforms, such as Amazon's Mechanical Turk and CrowdFlower, bring together millions of surveys and millions of survey participants, which means paying a flat rate for each completed survey may lead to survey responses that lack desirable care and forethought. Rather than allowing survey takers to maximize their reward by completing as many surveys as possible, we demonstrate how strategic incentives can be used to actually reward information and honesty rather than just participation. The incentive structures that we propose provide scalable solutions for the new paradigm of survey and active data collection.

  • Human-Machine Cooperation

    Iyad Rahwan

    Since Alan Turing envisioned Artificial Intelligence (AI), a major driving force behind technical progress has been competition with human cognition (e.g. beating humans in Chess or Jeopardy!). Less attention has been given to developing autonomous machines that learn to cooperate with humans. Cooperation does not require sheer computational power, but relies on intuition, and pre-evolved dispositions toward cooperation, common-sense mechanisms that are difficult to encode in machines. We develop state-of-the-art machine-learning algorithms that cooperate with people and other machines at levels that rival human cooperation in two-player repeated games.

  • Moral Machine

    Edmond Awad, Sohan Dsouza, Iyad Rahwan, Azim Shariff and Jean-François Bonnefon

    The Moral Machine is a platform for gathering a human perspective on moral decisions made by machine intelligence, such as self-driving cars. We generate moral dilemmas, where a driverless car must choose the lesser of two evils, such as killing two passengers or five pedestrians. As an outside observer, people judge which outcome they think is more acceptable. They can then see how their responses compare with other people. If they are feeling creative, people can also design their own scenarios, for others to view, share, and discuss.

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  • Promoting Cooperation through Peer Pressure

    Iyad Rahwan

    Cooperation in a large society of self-interested individuals is notoriously difficult to achieve when the externality of one individual's action is spread thin and wide on the whole society (e.g., in the case of pollution). We introduce a new approach to achieving global cooperation by localizing externalities to one's peers in a social network, thus leveraging the power of peer-pressure to regulate behavior. Global cooperation becomes more like local cooperation.