🏅Nobel Lectures (2000-)

"Scientists tell us that the world of nature is so small and interdependent that a butterfly flapping its wings in the Amazon rainforest can generate a violent storm on the other side of the earth. This principle is known as the 'Butterfly Effect". Today, we realize, perhaps more than ever, that the world of human activity also has its own "Butterfly Effect'" (Kofi Annan, [Nobel lecture] 2001) 

"[...] the boundaries between chemistry, biology, physics and medicine are rapidly disappearing. Only a comprehensive understanding of the chemical and physical processes in our cells and organ systems will yield the insights needed to develop rational approaches to the prevention and treatment of disease." (Avram Hershko, [Nobel lecture] 2004)

"In many real-world situations, cooperation may be easier to sustain in a long-term relationship than in a single encounter. Analyses of short-run games are, thus, often too restrictive. Robert Aumann was the first to conduct a full-fledged formal analysis of so-called infinitely repeated games. His research identified exactly what outcomes can be upheld over time in long-run relations." (Robert Aumann, [Nobel lecture] 2005) 

"Humans have a more complex motivational structure and more capability to solve social dilemmas than posited in earlier rational-choice theory. Designing institutions to force (or nudge) entirely self-interested individuals to achieve better outcomes has been the major goal posited by policy analysts for governments to accomplish for much of the past half century. Extensive empirical research leads me to argue that instead, a core goal of public policy should be to facilitate the development of institutions that bring out the best in humans."  (Elinor Ostrom, [Nobel lecture] 2009) 

"We should continue to use simple models where they capture enough of the core underlying structure and incentives that they usefully predict outcomes. When the world we are trying to explain and improve, however, is not well described by a simple model, we must continue to improve our frameworks and theories so as to be able to understand complexity and not simply reject it." (Elinor Ostrom, [Nobel lecture] 2009)

"Like writing, reading is a protest against the insufficiencies of life. When we look in fiction for what is missing in life, we are saying, with no need to say it or even to know it, that life as it is does not satisfy our thirst for the absolute – the foundation of the human condition – and should be better. We invent fictions in order to live somehow the many lives we would like to lead when we barely have one at our disposal." (Mario V Llosa, [Nobel lecture] 2010)

"Part of a meaningful quantitative analysis is to look at models and try to figure out their deficiencies and the ways in which they can be improved. A more subtle challenge for statistical methods is to explore systematically potential modeling errors in order to assess the quality of the model predictions. This kind of uncertainty about the adequacy of a model or model family is not only relevant for econometricians outside the model but potentially also for agents inside the models." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"Uncertainty, generally conceived, is not often embraced in public discussions of economic policy. When uncertainty includes incomplete knowledge of dynamic responses, we might well be led away from arguments that 'complicated problems require complicated solutions'. When complexity, even formulated probabilistically, is not fully understood by policy makers, perhaps it is the simpler policies that are more prudent. This could well apply to the design of monetary policy, environmental policy and financial market oversight. Enriching our toolkit to address formally such challenges will improve the guidance that economists give when applying models to policy analysis." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"Using random processes in our models allows economists to capture the variability of time series data, but it also poses challenges to model builders. As model builders, we must understand the uncertainty from two different perspectives. Consider first that of the econometrician, standing outside an economic model, who must assess its congruence with reality, inclusive of its random perturbations. An econometrician’s role is to choose among different parameters that together describe a family of possible models to best mimic measured real world time series and to test the implications of these models. I refer to this as outside uncertainty. Second, agents inside our model, be it consumers, entrepreneurs, or policy makers, must also confront uncertainty as they make decisions. I refer to this as inside uncertainty, as it pertains to the decision-makers within the model. What do these agents know? From what information can they learn? With how much confidence do they forecast the future? The modeler’s choice regarding insiders’ perspectives on an uncertain future can have significant consequences for each model’s equilibrium outcomes." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"When confronted with multiple models, I find it revealing to pose the resulting uncertainty as a two-stage lottery. For the purposes of my discussion, there is no reason to distinguish unknown models from unknown parameters of a given model. I will view each parameter configuration as a distinct model. Thus a model, inclusive of its parameter values, assigns probabilities to all events or outcomes within the model’s domain. The probabilities are often expressed by shocks with known distributions and outcomes are functions of these shocks. This assignment of probabilities is what I will call risk. By contrast there may be many such potential models. Consider a two-stage lottery where in stage one we select a model and in stage two we draw an outcome using the model probabilities. Call stage one model ambiguity and stage two risk that is internal to a model." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"When there is a reference to a decision problem, an analysis with multiple priors can deduce bounds on the expected utility consequences of alternative decisions, and more generally a mapping from alternative priors into alternative expected outcomes." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)

"Why is it fruitful to consider model misspecification? In economics and as in other disciplines, models are intended to be revealing simplifications, and thus deliberately are not exact characterizations of reality; it is therefore specious to criticize economic models merely for being wrong. The important criticisms are whether our models are wrong in having missed something essential to the questions under consideration." (Lars P Hansen, "Uncertainty Outside and Inside Economic Models", [Nobel lecture] 2013)