By Gisele Umbhauer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Part I: Interaction and Macro-Struture: an Overview Economies with Interacting Agents By Alan P. Kinnan (GREQUAM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Spatial Interactions in Dynamic Decentralised Economies: a Review By Giorgio Fagiolo (EUI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Network, Interactions between Economic Agents and Irreversibilities: the Case of the Choice among Competing Technologies By Patrick Cohendet (BETA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Rationality and Heterogeneity in Stochastic Aggregation Models By Jean-Michel Dalle (IEPE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Part II: Local Interaction, Learning and Diversity Networks Competition under Local Interaction and Behavioral Learning By Nicolas Jonard (BETA), Patrick Llerena (BETA) and Babak Mehm- pazir (BETA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Can Neighborhood Protect Diversity By Gisele Umbhauer (BETA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Interaction of Local Interactions: Localized Learning and Network Exter nalities By Nicolas Jonard (BETA) and Murat ytldlZoglu(BETA) . . . . . . . . . . . . 189 viii List of Contribution Evolution of Cooperation with Local Interactions and Imitation By Vanessa Oltra (BETA) and Eric Schenk (BETA) . . . . . . . . . . . . . . . . . . 205 On the Frontier: Structural Effects in a Diffusion Model based on Influence Matrixes By Alexandre Steyer (REC) and Jean-Benoit Zimmermann (GREQUAM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Part III: Behaviors, Externalities and the Emergence of Networks Networks, Specialization and Trust By Ehud Zuscovitch (BETA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Network Externalities, Cost FUnctions and Standardization By Hubert Stahn (BETA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 The Emergence of Network Organizations in Processes of Technological Choice: a Viability Approach By Jean-Pierre Aubin (IIASA) and Dominique Foray (IRIS-TS) . . . . . . 283 Are more Informed Agents able to shatter Information Cascades in the Lab? By Marc Willinger (BETA) and Anthony Ziegelmeyer (BETA) . . . . . . ."

Patrick Llerena and Mireille Matt BETA, Strasbourg, E-mail: pllerena@coumot. u-strasbg. fr BETA, Strasbourg, E-mail: matt@coumot. u-strasbg. fr 0. 1 Why Analyze Innovation Policies From a Knowledge- Based Perspective? It is broadly accepted that we have moved (or are moving) to a knowled- based economy, characterized at least by two main features: that knowl edge is a major factor in economic growth, and innovation processes are systemic by nature. It is not surprising that this change in the economic paradigm requires new analytical foundations for innovation policies. One of the purposes of this book is to make suggestions as to what they should include. Underpinning all the chapters in this book is a conviction of the impor tance of dynamic and systemic approaches to innovation policy. Nelson (1959) DEGREES and Arrow (1962) DEGREES saw innovation and the creation of new knowl edge as the emergence and the diffusion of new information, characterized essentially as a public good. The more recent theoretical literature regarded the rationale for innovation policies as being to provide solutions to "mar ket failures." Today, however, knowledge is seen as multidimensional (tacit vs. codified) and open to interpretation. Acknowledging that the creation, coordination and diffusion of knowledge are dynamic and cumu lative processes, and that innovation processes result from the coordination of distributed knowledge, renders the "market failure" view of innovation policies obsolete. Innovation policies must be systemic and dynamic."

Patrick Llerena and Mireille Matt BETA, Strasbourg, E-mail: pllerena@coumot. u-strasbg. fr BETA, Strasbourg, E-mail: matt@coumot. u-strasbg. fr 0. 1 Why Analyze Innovation Policies From a Knowledge- Based Perspective? It is broadly accepted that we have moved (or are moving) to a knowled- based economy, characterized at least by two main features: that knowl edge is a major factor in economic growth, and innovation processes are systemic by nature. It is not surprising that this change in the economic paradigm requires new analytical foundations for innovation policies. One of the purposes of this book is to make suggestions as to what they should include. Underpinning all the chapters in this book is a conviction of the impor tance of dynamic and systemic approaches to innovation policy. Nelson (1959) DEGREES and Arrow (1962) DEGREES saw innovation and the creation of new knowl edge as the emergence and the diffusion of new information, characterized essentially as a public good. The more recent theoretical literature regarded the rationale for innovation policies as being to provide solutions to "mar ket failures." Today, however, knowledge is seen as multidimensional (tacit vs. codified) and open to interpretation. Acknowledging that the creation, coordination and diffusion of knowledge are dynamic and cumu lative processes, and that innovation processes result from the coordination of distributed knowledge, renders the "market failure" view of innovation policies obsolete. Innovation policies must be systemic and dynam