In my own thinking they have never been separate. Motivation for the purer theory came almost exclusively from preoccupation with (and fascination with) “applied” problems; and the clarification of theoretical ideas was absolutely dependent on an identification of live examples Corruption is not simply an aberrant act committed by a reprobate actor; it is a repudiation of the idea that a fabric of shared values is necessary to undergird societies and governments Faced with a topic like corruption, the first task is to disaggregate the types of corruption, their scope and seriousness, the beneficiaries and the losers. One approaches a sensitive subject by highlighting not the moral failures of the individuals but the structural failures of information and incentives. One uses a simplifying theory to obtain not an optimizing model under restrictive assumptions but a heuristic [analytical] framework that enables the problem solvers to address the complex problem of corruption in their varied and unique circumstances. Finally, one tries to illuminate both the utility and limitations of the framework by looking at real examples. [Corruption] breaks down trust, confidence and the rule of law … [It] may serve organizational interest, especially when it subverts unnecessary bureaucratic red tape. More commonly, thought, corruption in organizations has disastrous effects on merit systems, the marshaling and allocation of resources, and the pursuit of the organizations’s primary mission … It is important to notice who gains and who loses from corrupt act. This identification aids in identifying those forces likely to support and to oppose anti-corruption programs. Often policymakers cannot readily choose agents or change rewards and penalties or restructure organizations or alter agents’ attitudes and ethical beliefs. Yet through a variety of informational measures they can make it more likely that agents’ illicit activities are undercover…It is precisely the lack of information – an abundance of ignorance and uncertainty – that characterizes the environment [where corruption thrives] … Agents will correctly see that their illicit cannot be detected, and they will continue to be corrupt. Poor information is a major source of corruption, and better information is a partial cure (but surely not a cure-all). The problem of corruption is that it tends to become the Problem of Corruption. Moral issues usually obscure practical issues, even where the moral question is a relatively small one and the practical matter is very great. There is one way to find out if a man is honest; ask him! If he says yes you know he’s crooked. When thinking of corruption in a particular context, modeling policies in a systemic way, i.e. developing a theoretical framework for policy analysis], has practical relevance in two senses. First, it can be used as heuristic device for public managers to consider a series of alternatives, suggesting options that might otherwise have been overlooked. Second, when studying anti-corruption efforts elsewhere, we may find this framework helpful for understanding why and how these efforts succeeded or did not [by identifying relevant mechanisms]. This is about as much as we should hope for any conceptual model applied to public policies. If our goal is to be of practical use to policy-makers, we academicians would do better to derive rough-and-ready frameworks and checklists instead of calculating theoretically “optimal” policies under highly restrictive and unrealistic conditions. We might think of our job as stimulating creativity, making sure promising options are not overlooked, and highlighting tradeoffs – a much humbler stance than many social sciences and policy advisers are used to but, I think, the correct one. The pursuit of wealth is now largely the pursuit of information and its application to the means of production. The rules, customs, skills and talents necessary to uncover, capture, promote, preserve and exploit information are now humankind’s most important assets. The competition for the best information has replaced the competition for the best farmland or coalfields. In the past when the method of creating wealth changed, old power structures lost influence, new ones arose, and every facet of society was affected. As we can already see the beginning of that process in this revolution, one can postulate that in the next few decades the attraction and management of intellectual capital will determine which institutions and nations will survive, and which will not. A key to success must surely be theories built in the service of actual [application] data, not theories for their own sake. A data-driven understanding of human actions could help us to translate in to a predictive mathematical language the fundamental principles that drive a society;s collective behavior. In a world in which all event are recorded by computers, the conditions for this research are increasingly in place. It is a capital mistake to theorize before one has data. The sexy job in the next ten years will be statisticians… The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it. When the test of the truth of a relationship lies finally in the data themselves [and] nature, however stretched out on the rack, still has a chance to say ‘No!’ – then the subject is a science. Putting the theory after the history constitutes what I regard as the correct approach to analysis: theories ought to be inferred from facts, and not the other way around. Of course, there is no such thing as a pure confrontation with facts, devoid of prior theoretical constructs. Those who think they are empirical in that fashion are deluding themselves. But all too often social science begins with an elegant theory and then searches for facts that will confirm it. I would rather guess what follows from more-relevant assumptions than derive precise conclusions from less-relevant assumptions. It is often said that the mathematical rigor of contemporary economic theory is due to the economists’ ‘physics envy.’ In fact, physicists generally judge models according to their ability to account for the facts, not their mathematical rigor. Physicists generally believe that rigor is the enemy of creative physical insight and they leave rigorous formulations to the mathematicians. The economic theorists’ overvaluation of rigor is a symptom of their undervaluation of explanatory power. The truth is its own justification and needs no help from rigor. Essentially, all models are wrong, but some are useful. When thinking of corruption in a particular context, modeling policies in a systemic way, i.e. developing a theoretical framework for policy analysis], has practical relevance in two senses. First, it can be used as heuristic device for public managers to consider a series of alternatives, suggesting options that might otherwise have been overlooked. Second, when studying anti-corruption efforts elsewhere, we may find this framework helpful for understanding why and how these efforts succeeded or did not [by identifying relevant mechanisms]. This is about as much as we should hope for any conceptual model applied to public policies. If our goal is to be of practical use to policy-makers, we academicians would do better to derive rough-and-ready frameworks and checklists instead of calculating theoretically “optimal” policies under highly restrictive and unrealistic conditions. We might think of our job as stimulating creativity, making sure promising options are not overlooked, and highlighting tradeoffs – a much humbler stance than many social sciences and policy advisers are used to but, I think, the correct one. The key difference between the Numerati [people who are developing and implementing methods and technologies to capture and analyze our everyday activities] and the rest of us lies in the toolbox they carry. It contains sets of mathematical formulas and drawers full of algorithms that mankind has been building for thousands of years. Using this know-how, they attempt to put complex reality into numbers so that theories can be tested and refined. Even though statisticians admit that models are always ‘wrong’ insofar as they represent only educated guesses, they are certain that what they do benefits society. They are able to see the virtue of being wrong. The predictions of a model are only as good as the assumptions that go into it; if one wrongly estimates the probability of, say, a decrease in housing prices, all the conclusions of the model will be wrong. …[C]ritical scientific reasoning almost always involves a component of intuition, and intuition is almost always informed by experience and hard knowledge won by reasoning things out. When Einstein was working on his theory of special relativity, he had a ‘hunch’ that energy and matter were different versions of the same thing. Not until he worked out the equations using his astounding powers of critical reasoning, arriving at the famous E=mc^2, was his hunch worth a damn. In the wake of the financial crisis naïve extremists want to do away with financial models completely, imagining that humans can proceed on purely empirical grounds. Conversely, naïve idealists pin their faith on the belief that somewhere just offstage there is a model that will capture the nuances of markets, a model that will do away with the need for common sense. The truth is somewhere in between. Life’s most important questions are, for the most part, nothing but probability problems. The question is not whether these experts are well trained. It is whether their world is predictable.
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