Scott de Marchi is Professor of Political Science and Director of Decision Science at Duke University. He is a principal investigator for the for the National Science Foundation's Empirical Implications of Theoretical Models program and his research focuses on decision-making in contexts that include Congress, coalition and crisis bargaining, and interstate conflict. Michael Laver is Emeritus Professor of Politics at New York University. He has published 20 books, including Multiparty Government: The Politics of Coalition in Europe (1991), Making and Breaking Governments (1996), and Party Competition: An Agent-Based Model (2014).

'When what works in theory fails in practice, it is time to think differently. Here, de Marchi and Laver develop a new theoretical approach for understanding the governance cycle in parliamentary democracies. By skillfully melding ideas from fields such as game theory, behavioral decision making, AI, and the computational modeling of complex systems, they produce key new insights into the behavior of political systems while also providing a masterful demonstration of the theoretical potential of 21st century social science.' John Miller, Carnegie Mellon

• Introduction; 1. Governance, complexity, computation and rationality; 2. The governance cycle; 3. Agent based model of government formation and survival; 4. Artificial intelligence and government formation; 5. Analyzing models of government formation and survival; 6. The empirics of government formation and survival; 7. Conclusions and aspirations; Appendices.

Scott de Marchi is Professor of Political Science and Director of Decision Science at Duke University. He is a principal investigator for the for the National Science Foundation's Empirical Implications of Theoretical Models program and his research focuses on decision-making in contexts that include Congress, coalition and crisis bargaining, and interstate conflict. Michael Laver is Emeritus Professor of Politics at New York University. He has published 20 books, including Multiparty Government: The Politics of Coalition in Europe (1991), Making and Breaking Governments (1996), and Party Competition: An Agent-Based Model (2014).

'When what works in theory fails in practice, it is time to think differently. Here, de Marchi and Laver develop a new theoretical approach for understanding the governance cycle in parliamentary democracies. By skillfully melding ideas from fields such as game theory, behavioral decision making, AI, and the computational modeling of complex systems, they produce key new insights into the behavior of political systems while also providing a masterful demonstration of the theoretical potential of 21st century social science.' John Miller, Carnegie Mellon

• Introduction; 1. Governance, complexity, computation and rationality; 2. The governance cycle; 3. Agent based model of government formation and survival; 4. Artificial intelligence and government formation; 5. Analyzing models of government formation and survival; 6. The empirics of government formation and survival; 7. Conclusions and aspirations; Appendices.

Scott de Marchi is Professor of Political Science and Director of Decision Science at Duke University. He is a principal investigator for the for the National Science Foundation's Empirical Implications of Theoretical Models program and his research focuses on decision-making in contexts that include Congress, coalition and crisis bargaining, and interstate conflict. Michael Laver is Emeritus Professor of Politics at New York University. He has published 20 books, including Multiparty Government: The Politics of Coalition in Europe (1991), Making and Breaking Governments (1996), and Party Competition: An Agent-Based Model (2014).

'When what works in theory fails in practice, it is time to think differently. Here, de Marchi and Laver develop a new theoretical approach for understanding the governance cycle in parliamentary democracies. By skillfully melding ideas from fields such as game theory, behavioral decision making, AI, and the computational modeling of complex systems, they produce key new insights into the behavior of political systems while also providing a masterful demonstration of the theoretical potential of 21st century social science.' John Miller, Carnegie Mellon

• Introduction; 1. Governance, complexity, computation and rationality; 2. The governance cycle; 3. Agent based model of government formation and survival; 4. Artificial intelligence and government formation; 5. Analyzing models of government formation and survival; 6. The empirics of government formation and survival; 7. Conclusions and aspirations; Appendices.