BY S. M. HARRIS

Copyright © 2022. All rights reserved.

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DEDICATION

To the True Believers of All Persuasions

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EPIGRAPH

Albert Einstein quote: “Perfection of means and confusion of ends seem to characterize our age.”

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F. A. Hayek quote: “We shall not grow wiser before we learn that much of what we have done was very foolish.”

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When we expand the scope of our problems to include all possible future states of the world a structure of universal values emerges. Understanding the process by which we best pursue these invariant ends can help us live ever more wisely.

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PREFACE

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BOUNDLESS REASON, A UNIVERSAL STRATEGY FOR DECIDING WELL is pioneering work in the science of deciding well. In this science, we learn to decide ever better by applying the process of deciding well to itself. Beneath this science is mathematics. Beneath mathematics is a form of reason based not only on logic but also on the beauty that emerges from removing “wasted” resources from the process of deciding well WHERE DECIDING WELL IS A MATTER OF LEARNING TO DECIDE EVER MORE WISELY. To decide well, we use logical models of parts of the world to predict well. Predictions help us evaluate solutions to given problems. We also use models that “ring true” with all we currently know about deciding well to explain well. Explanations help us find “beautiful” problems to solve in deciding well.

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We can see the need for this boundless approach to science most clearly in the failure of modern (received/reductionist/bounded) science to explain learning well. To judge what we need to learn, we need to judge what we need to do. From the view of modern science, judging what we need to do is beyond the realm of science. From the “boundless” view of this work, it is a part of science. To decide well, we base research programs for predicting well on theories for predicting well, and research programs for explaining well on a universal strategy for deciding well. Using theories that predict well to explain tends to blind us to the best problems to solve, which are those that include the possibility of creating knowledge resources. Unlike non-knowledge resources, using knowledge resources does not use them up. Once in use, knowledge resources are inexhaustible.

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Modern science helps us to make the best use of what we currently believe we know. In contrast, the science of deciding well also considers what we may learn. Consider the problem of computing π. The reason of modern science (logic) tells us to use the means not yet disproven empirically to be the best for computing π to our chosen level of accuracy. In contrast, the reason of the science of deciding well (“boundless reason”) also tells us how to learn to compute π ever better. For computing π to a million decimal places, logic is reason enough. For computing π to a quintillion places, boundless reason is much better than logic alone. Creating significantly better means of computing π to a quintillion places will make the best program based on current means obsolete long before it finishes computing. We fuel the creation of better means with the resources we need to create better means.

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This book provides a universal strategy for learning everything worth learning, not a theory for learning everything worth learning, much less a theory of everything. If I have done my job well, people will read it more than once. Upon each reading, they will find better problems to solve, if not in it, then in what new they bring to it.

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S. M. Harris

Southwest Florida

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ACKNOWLEDGMENTS

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A poster in my high school physics classroom proclaimed, “THE PROBLEM, ONCE SOLVED, IS SIMPLE.” To this, I add, “THE PROBLEM, ONCE FOUND, IS OBVIOUS.” Rather than choosing to acknowledge the countless people who helped me refine this work, I prefer to recognize some of the people who helped me find the problems that led to it.

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The first two were sons of bankers from Grinnell, Iowa, a “new Jerusalem” shaken by the scandalous collapse of its most trusted bank in 1904, which took over twenty years to work out. John Huntington Harris expressed great contempt for people who too readily reduced the world to numbers without considering the use of these numbers. He acquired this habit while rising through the ranks of the Organizational Planning and Statistical Control Divisions of the United States Army Air Forces Management Control Directorate. Wilfred “Mac” McNeil told me parables based on his experiences as the special assistant for financial matters to the first secretary of the Department of Defense and comptroller under its next five secretaries. Both had faith in people working together in living well.

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The next five were Pomona College professors. Frederick Sontag gently pushed me never to stop becoming more than I am. For a third of a century, Fred was extremely generous with his most precious resource, his time. Jay Atlas introduced me to the pragmatic philosophies of W. V. O. Quine and Morton White. Richard McKirahan explained why we can never simultaneously find pleasure in watching a play and in eating peanuts. James Likens told me that economists do not do dynamics well and that social scientists tell many stories about the same complex phenomenon. In my last semester in college, I took an independent study course in human capital theory from Gordon Douglass. This course exposed me to economic methodology, a subject so dangerous to the mental health of economists that George Stigler once joked that economists ought to leave it to the end of their careers. I could not get my mind around how a theory could be both useful in predicting what will happen in markets and foolish in explaining what to do. The harder I tried to understand this apparent conflict between truth and wisdom, the more distraught I became. Despite the threat of failing to graduate, I could not write the required term paper.

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Following these were five Stanford Graduate School of Business professors. Charles Holloway and Charles Horngren taught me how decision science and accounting models color analysis. William Beaver showed me the wisdom of applying economic analysis to accounting models. Harold Leavitt told me that the least understood and most often ignored part of making decisions is finding the best problem to solve. George Leland Bach, another son of a Grinnell banker, taught an ethics course that became the exemplar for accrediting collegiate schools of business.

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In the early 1980s, I was head of information systems and human resources at a privately held business forms company. Our firm had grown thirtyfold in a dozen years while generating cash. I believed that we owed much of our success to our ability to learn faster than others learned. A lecture by Taiichi Ohno in Chicago and a subsequent tour of factories practicing just-in-time manufacturing in Japan convinced me that we had much to learn about learning well.

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After selling my interest in the business forms business in 1985, I decided to explore an idea I had at Stanford about the design of a visually-oriented computer language for learning in financial decision-making. While waiting for computer technology to catch up to what I had in mind, I had much time to revisit the modern economic problem of learning. In 1992, I gave Gordon Douglass the term paper I was unable to write seventeen years earlier, “Wealth in the Information Age.” The concept of excellence in consumption at the core of this paper led me to write an essay on modern monetary policy, which I delivered to the International Schumpeter Society conference in Athens in the fall of 1993. A little more than a year later, I joined the Santa Fe Institute Business Network. There I met three seekers of larger truths. Howard Sherman introduced me to Albert Einstein’s theory of knowledge. Nicholas von Neumann, brother and biographer of John von Neumann, told me he planned to explain human history. Brian Arthur suggested that I write my book about the economics of learning “from the heart.” Writing what evolved into this book became a higher priority than selling the financial analysis language, which has too little of the sweet pretense of certainty for modern tastes.

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INTRODUCTION

Russian proverb: The wise man says, “I am looking for the truth,” and the fool, “I have found the truth.”

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In 1949, University of Chicago-trained economist George Leland Bach became the founding dean of the Graduate School of Industrial Administration at the Carnegie Institute of Technology. He envisioned a school based on management science. In 1962, he left what some have called the first modern business school for a teaching job at Stanford University. In the fall of 1978, I took his MBA core course in ethical management. From the first day until the last, Dean Bach challenged our solutions to cases without disclosing his values. After we finished the last case, he told us three rules he used to judge his answers. These were the Golden Rule, the television rule, and the long-run rule: Treat others as you would like them to treat you. Assume that your actions will become widely known. Don’t eat your seed corn.

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I wanted something more coherent and complete than these three rules. I wanted something to help me know when analytical tools were leading me astray. I have since learned that I ought to have wanted a SCIENCE OF DECIDING WELL, a not-yet-disproven method of telling the best way forward.

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Some modern thinkers will claim that I confuse seeking truth with seeking wisdom. They confuse events and processes. In seeking truth, they confuse taking the next step toward seeking truth with seeking truth. In seeking wisdom, they confuse taking the next step toward seeking wisdom with seeking wisdom. In doing so, they fail to refine the process of refining everyday thinking, hence fail to make the best use of what they currently know. To correct this mistake, I propose a learning-oriented model of deciding well that holds true regardless of beliefs and circumstances. This invariant model consists of a sequence of three basic steps: choosing a temporally bounded problem to solve, attempting to solve this problem well, and learning from the experience. So conceived, deciding well is an economic process, a process subject to constraints. Scarcity hinders us not only from solving given problems but also from finding better problems to solve and from learning from experience. The economics of deciding well concerns not only efficiency but also effectiveness and wisdom.

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The most fundamental problem we face in deciding well is knowing whether the problem we believe is best is truly best. Dwight Eisenhower provided us with a maxim for finding problems to solve that we can use to address this problem: “If a problem cannot be solved, enlarge it.” Following this simple advice completely yields the problem that contains all other problems in deciding well. Our problem becomes one of how best to address this universal problem.

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We will never know how to build logically consistent and complete models of this universal problem. The best we can do is build a complex (multiple-frame) model that provides a grand strategy for addressing it. As military strategist John Boyd wrote, grand strategies ought to provide us with the ability to peer into and discern the inner nature of things; the internal drive to think and act without being urged; the power to adjust or change in order to cope with new or unforeseen circumstances; and the power to perceive or create interaction of apparently disconnected events or entities in a connected way.

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Addressing this universal problem well calls for considering the knowledge resources that we need to address it well. We may call universally useful forms of these resources BOUNDLESS FACTORS OF DECIDING WELL. These factors form a coherent, self-refining system of universal values:

For any boundless factor of deciding well (x) and any other boundless factor of deciding well (y), pursuing x well calls for us to decide well, which in turn calls for us to pursue y well. Further, pursuing y well calls for us to decide well, which in turn calls for us to pursue x well. Hence, the pursuit of x and the pursuit of y intertwine to form a complex pursuit in which the better we decide, the more tightly the endless pursuits of these two factors intertwine. Applying this reasoning to all boundless factors of deciding well, the endless pursuits of all boundless factors of deciding well intertwine to form a complex pursuit in which the better we decide, the more tightly the endless pursuits of these boundless factors intertwine.

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We can use these relations to build belief system structures for finding “beautiful” problems to solve, problems that “ring true” with all we currently know about deciding well WHERE DECIDING WELL IS A MATTER OF LEARNING TO DECIDE EVER MORE WISELY.

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This formal concept of beauty allows us to change what Albert Einstein called “the whole of science” from the products to the process of refining everyday thinking. However, there is a catch. In his book, THE STRUCTURE OF SCIENTIFIC REVOLUTIONS, Thomas Kuhn claimed that science is subject to periodic revolutions. Kuhn likened these “paradigm shifts” to changing from seeing one thing to another in a drawing designed to let people see one or the other but not both simultaneously (e.g., the trick drawing of a duck and a rabbit). Changing our concept of the whole of science to the process of refining everyday thinking is no ordinary paradigm shift. It calls for training ourselves to assemble the pursuits of boundless factors of deciding well into a whole. We may liken this to seeing the three-dimensional image in an imperfect autostereogram (e.g., a Magic Eye picture). Seeing these images calls for training ourselves to assemble two-dimensional facets into three-dimensional wholes. Similarly, making sense of this grand strategy calls for us to assemble timeless pursuits of boundless factors of deciding well into a whole.

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What follows is pioneering work in the science of deciding well. The basis of this science is a grand strategy for deciding well, not a theory for deciding well. TIMELESS RELATIONS DEFINE THIS STRATEGY. TEMPORALLY DEFINED OR CONSTRUCTED DETAILS ONLY SERVE TO HELP US MAKE SENSE OF IT. In this, they are like the details in compendious drawings of Mount Fuji in which perspectives and seasons change, but the mountain itself does not. Bold strokes define the scenes. Details help us to make sense of them.

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The first chapter, “Deciding Well,” explains the reason underlying this “superrational” strategy for learning how to decide ever better. It ends by explaining why the Information Age is but the threshold of the Learning Age.

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The next six chapters portray pursuits of six likely boundless factors of deciding well. “Living Well” provides “recursionist” compliments to the marginalist economic concepts of wealth, consumption, trade, production, taxation, and profit. “Contemplating Well” explores the role of beauty in deciding well. “Believing Well” describes the process of refining everyday thinking. “Governing Ourselves Well” argues that it is wise to think of governments as experiments that test the stories we use to assign rights and responsibilities. It goes on to claim that the best such story is the one that calls for us to decide well. “Linking Well” outlines a spiritual end that both materialists and dualists can agree to pursue. In doing so, it explores what Einstein believed stands at the cradle of true art and true science. “Competing Well” refines Douglas Hofstadter’s concept of superrationality, John Boyd’s grand strategy for winning, and the modern concept of evolution.

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The last chapter, “Reasoning Well,” begins by restating the case for the reason underlying the science of deciding well. Arguably, this is the reason that game theory pioneer John von Neumann sought in his unfinished study of the mathematics of reason, which Yale University Press published posthumously as THE COMPUTER AND THE BRAIN. The chapter ends with a Platonic definition of the timeless end of reasoning well.

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Understanding this complex form of reason calls for changing the meaning of some familiar terms and phrases. To make this a bit easier, I italicized the first instance of terms and phrases with unfamiliar meanings. I also distinguished between meanings (concepts) and containers for meanings (terms) by surrounding concepts with double quotation marks and terms with single quotation marks. For example, the meaning of ‘up’ depends on the context in which we use it. In the context of a conventional wall map, the meanings of ‘up’ and ‘north’ are the same. In the context of a globe, the meanings of ‘up’ and ‘north’ differ.

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First Chapter: DECIDING WELL

Kong Qiu quote: “If words (designations, concepts) are not right, judgments are not clear; works do not prosper; punishments do not strike the right man; and people do not know where to set hand and foot.”

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All beings bound to live well in the flow of time seek to survive and thrive by taking order into and discarding disorder from themselves and their environments. This book provides a research program for deciding well based on this simple insight into the business of life. Although we can never know exactly what we need to decide well, we can know that we need ever-better knowledge of deciding well. Acquiring this knowledge well calls for us to understand how best to reduce our perceptions to descriptions of the world. The atoms of these descriptions are concepts, the smallest of tools for thinking and communicating about living well in the flow of time.

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Concepts are a type of KNOWLEDGE RESOURCE. Knowledge resources are useful forms (patterns) of matter, energy, space, and time. Knowledge is the form of material in a printed book, not the material itself. Similarly, it is the form of material in DNA, not the material itself.

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Knowledge resources profoundly differ from other resources. Although they can be costly to create, copy, and place into use, they are free once in use. USING KNOWLEDGE DOES NOT EXHAUST IT.

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The inexhaustibility of knowledge gives concepts IN USE an advantage over concepts NOT YET IN USE. When we combine knowledge into a network, this “lock-in” effect can withstand superior knowledge not yet in use. These networks include our systems of beliefs. Once we have learned to view the world in one way, it can be challenging for us to view it in another. However, we must do this if we are to decide well when to decide well includes learning to decide ever better.

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CHOOSING FRAMES WELL

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We may call systems of conceptual tools for planning, acting, and learning from experience LANGUAGE. We also may call beings bound to live well in the flow of time who have learned to use language PEOPLE. As people, we reduce our sensations of the world to concepts, which we arrange into structures that help us solve what we believe are similar problems. We may call these conceptual structures FRAMES.

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The frames we use affect how we think about the world. Consider some of the many ways in which we may think about deciding well. One way is to think about how we overcome scarcity of time, knowledge resources, and material resources. From within this frame, the meaning of the term ‘well’ in the phrase ‘deciding well’ concerns excellence in using resources.

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A second way we may think about deciding well is to think about the method we use to decide. What method do we choose? DELIBERATING, which is to say deciding meticulously, is thorough but costly in time and other resources. Using DECISION RULES, rules of thumb and other heuristic methods, is less costly but less thorough. Using DISCIPLINE, consciously formed habits, is the least thorough but least costly and most resistant to deprivation. From within this frame, the meaning of the term ‘well’ in the phrase ‘deciding well’ concerns excellence in matching the method we use to the problem we face.

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A third way we may think about deciding well is to think about how we frame the world. From within this frame, the meaning of the term ‘well’ in the phrase ‘deciding well’ concerns excellence in choosing frames. But what exactly does “excellence in choosing frames” mean? In defining the concept of excellence in choosing frames, we must choose a frame. To choose this frame, we must choose a frame from within which to choose. To choose this frame, we must choose a frame from within which to choose. And so on to infinity. We cannot solve this infinitely large problem. However, we can address it well by making it part of the problem of deciding well. This self-referential approach to deciding well calls for knowing what makes frames useful in deciding well. We can then use this knowledge to build ever-better belief systems for finding problems to solve in deciding well.

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Frames Useful in Deciding Well

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In deciding well, we predict what will happen in parts of the world. We use this knowledge to assign probabilities to future events, which helps us solve given problems. To predict well, we use frames based on the world AS WE CURRENTLY FIND IT. In modern Western philosophical terms, these frames are POSITIVE.

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In deciding well, we also explain the world. We use this knowledge to find problems to solve. To explain well, we use frames based on the world AS WE OWE IT TO OURSELVES (OUGHT) TO FORM IT. In modern Western philosophical terms, these frames are NORMATIVE.

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Normative frames may be either TEMPORAL or TIMELESS. Temporal normative frames have ends (goals) bounded in time. Timeless normative frames have ends not bounded in time. Consider the first basketball game in the 1986 film HOOSIERS. Most town residents believe that the temporal end of winning this game is the highest priority. However, the new high school basketball coach values the timeless end of playing basketball well more highly.

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Temporal and timeless normative frames differ markedly in their formal concepts of EXCELLENCE IN MEANS. Excellence in means in pursuing temporal ends is EFFICIENCY, excellence in solving temporal problems. A formal decision EVENT consists of formulating solutions to a given temporal problem, evaluating these solutions, choosing a solution, and implementing the chosen solution. In contrast, excellence in pursuing timeless ends also includes EFFECTIVENESS, excellence in choosing problems to solve in pursuing timeless ends. A formal decision PROCESS is an endlessly repeating cycle of six steps: (1) finding a temporal problem to solve that appears to be in line with a given timeless end, (2) formulating various solutions to this problem, (3) evaluating these solutions, (4) choosing a solution, (5) implementing the chosen solution, and (6) learning from the experience.

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The Wisdom of Effectiveness

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When we use temporal frames to explain the world, we only explain parts of the world. Choosing which parts of the world to explain is part of deciding well. From modern views of science, choosing which parts of the world to explain is the business of the science of science policy. From the view of the science of deciding well, it is the business of the science of deciding well.

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We may think of the science of deciding well as the process of refining everyday thinking. We refine frames by removing waste from them. Sources of waste include logical contradictions, concepts represented by more than one term, terms that represent more than one concept, and (apparently) irrelevant facts about the world. We may call useful conceptual structures from which we have removed all waste currently economical for us to remove MODELS.

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In deciding well, we distinguish between temporal and timeless normative models. We can see this distinction in two models for deciding how often to set up machine tools. The first is the temporal ECONOMIC ORDER QUANTITY (EOQ) model. The second is the timeless RAPID TOOL SETTING (RTS) model.

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Suppose we expect to sell fifty thousand units of our new electric car. Each of these cars needs a left front door panel. The machine tool that makes these panels also makes other parts. Setting up tools uses resources. Storing parts also uses resources. How many of these panels should we make at once? At one extreme, we make one batch of fifty thousand panels. At the other extreme, we make fifty thousand batches of one panel. Between these two extremes lies the best number to make at once. The EOQ model yields the number at which the marginal cost and marginal benefit of ordering one more panel per batch just equal one another. This number maximizes the net benefit of setting up the tool FOR OUR CURRENT KNOWLEDGE OF HOW BEST TO SET UP THE TOOL.

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In using the temporal EOQ model, we assume that people do not learn through experience. This assumption tends to blind us to the possibility of learning. For example, most managers who use this model have their people follow set procedures for setting up tools. In contrast, managers who use the RTS model promote learning. They do so by such means as training their people to learn and rewarding them for learning.

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The RTS model combines an EOQ model with an expected learning curve for setting up the tool. The resulting model disturbs people who like neat solutions. It tells us that we can choose to learn more quickly by setting up more often. Solving this problem calls for evaluating what we expect to learn. The inexhaustibility of knowledge makes this exceedingly hard to do. Except in the special case in which we know precisely when and how we will use the new knowledge, we cannot estimate its value by calculating the value of the resources it replaces. Here, we do not know exactly when and how we and others will use new knowledge of how to set up, learn to set up better, and learn to learn better. Allowing for the possibility of learning turns simple closed-ended problems into complex open-ended ones.

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The benefits of short setup times go far beyond savings in capital and direct labor costs. Short setup times yield savings in warehouse space, material-handling equipment, material handlers, stock clerks, and other indirect labor. They also reduce scrap: When production team members set up wrongly, they scrap smaller batches. Short setup times even enhance learning: It is much easier for team members to remember what they did three hours ago that turned out to be wrong than what they did three weeks ago that turned out to be wrong.

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We cannot judge the full value of learning to rapidly set up tools without understanding its role in the rise of Toyota. In the late 1940s, Japan’s small market for trucks and lack of capital forced Toyota to produce trucks in small batches. A Toyota supervisor named Taiichi Ohno knew that his firm would never be able to compete against mass-producing American firms by making trucks the same way they did. Instead, he envisioned making batches of similar parts as efficiently as masses of identical parts. This vision called for cutting setup costs to almost nothing, matching mass-production quality, and precisely coordinating batch production. Ohno and his team did not know how to invent this knowledge directly. Instead, they invented a strategy for inventing this knowledge.

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Ohno’s learning strategy combines a simple, elastic, and robust means of linking processes (DUAL KANBAN) with continuous improvement (KAIZEN). The elastic links allow the control system to stretch to fit problems that are too complex for experts to solve by themselves. An early example was learning to produce several car models on a single assembly line. Slowly tightening stretched links uncovers small problems that team members can solve. Solving these small problems solves the complex problem. In swallowing and digesting complex problems, this strategy produces not only goods for sale but also knowledge of how to produce these goods better. Toyota teams first used this strategy to capture scale economies without scale. They have since used it to learn to produce in ever-better ways higher-quality products, a wider variety of products, and new products. With such great benefits, variants of the Toyota system swept through industry. For more about Ohno’s strategy for learning, see Appendix B (Lean Production).

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Seeing Through Apparent Miracles

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From modern views of producing well, as miraculous as the Toyota learning strategy results appear, the details appear paradoxical. Consider the following KAIZEN slogans:

GOOD QUALITY COSTS LESS THAN POOR QUALITY. When we consider good quality and poor quality in deciding well, good quality costs less. Good quality avoids rework, returns, low employee morale, and customer dissatisfaction. Only when we consider the costs of producing good quality versus poor quality, given a fixed stock of useful knowledge, that good quality costs more. Once we have learned how to make quality products as efficiently, good quality costs less.

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PRODUCING IN SMALL BATCHES COSTS LESS THAN PRODUCING IN LARGE ONES. Making products in small batches lowers capital costs and increases flexibility. Once we have learned how to make products in small batches as efficiently, small batches cost less.

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SMALLER, LIGHTER, NARROWER, AND SHORTER ARE BETTER THAN BIGGER, HEAVIER, WIDER, AND TALLER. Larger products cost more to package, transport, store, and recycle. Once we have learned how to make functionally identical products using fewer material resources as efficiently, doing so costs less.

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FINDING A PROBLEM IS LIKE FINDING A DIAMOND. Without a problem to solve, there can be no improvement.

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The Victorian novel FLATLAND: A ROMANCE IN MANY DIMENSIONS can provide us with insight into these apparent paradoxes. In this novel, author Edwin Abbott described the journey of A. Square, a skeptical resident of Flatland’s two-dimensional world, to Spaceland’s three-dimensional world. By seeing Flatland’s plane from above, this skeptic learns that such apparent Flatlander miracles as being able to see into a locked cupboard and suddenly appearing from nowhere are natural phenomena. When he returns to Flatland, A. Square cannot explain these apparent miracles to his peers, who cannot “see” (imagine) what he means when he says he went “up but not north.” Lacking the concepts that they need to “see” beyond their two dimensions, these Flatlanders fail to grasp a larger truth. To grasp this truth, they need concepts that allow them to “see” into the third dimension of height.

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Similarly, Toyota production team members find it impossible to explain their apparent miracles to people who believe that ‘excellence in means’ and ‘efficiency’ refer to the same concept. Lacking the concepts that they need to “see” beyond what they currently know, these people fail to grasp a larger truth. To grasp this truth, they need concepts that allow them to “see” deeply into the future.

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From a temporal view of deciding well, the idea of seeing deeply into the future is nonsense. It ignores uncertainty. How can we know what we don’t know when we don’t know what we don’t know? In contrast, from the view of deciding well put forth in this work, we can know what we need to address unexpected problems infinitely far into the future. WE CAN LEARN TO HANDLE UNEXPECTED EVENTS EVER BETTER. From this view of the whole of space and time, ignoring uncertainty is unreasonable.

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THE WISDOM OF WISDOM

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In the first basketball game in the film HOOSIERS, the coach valued the timeless end of playing basketball well more than the temporal end of winning the first basketball game. By the end of the film, we learn that he believed that playing basketball well is a means to the higher end of deciding well.

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We can see an analogue to the coach’s approach to playing basketball in mathematics. Nearly a century and a half ago, mathematician Georg Cantor proved that some infinitely large sets of numbers are larger than others. In 1878, he hypothesized that there is no infinitely large set of numbers having a number of elements strictly between the number of integers and the number of real numbers. Trying to prove or disprove this hypothesis drove him insane. In 1963, Paul Cohen showed that there exist approaches to mathematics where this hypothesis is true and other approaches where it is false. Which of these approaches ought we to choose? From the view of the science of deciding well, we ought to select all approaches that best help us decide well. Mathematics is a form of science, the science of forms. So conceived, the timeless end of mathematics is complete knowledge of the set of forms that are perfectly useful in deciding well. We can never prove any form is a member of this set. We can only disprove it is a member by finding more useful forms in deciding well. Pragmatically, we discover ideal forms and invent all other forms. For more about this natural approach to mathematics, see Appendix A (The Science of Forms).

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An Infinitely Large Crane

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In framing the world, we separate what lies within the frame from what lies outside it. In the EOQ/RTS example, we saw how temporal bounds tend to blind us to better problems to solve. This bounds-blindness claim also applies to timeless normative frames: PURSUING ANY GIVEN END WELL DOES NOT INCLUDE CHOOSING THIS END WELL.

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In theory, we can solve this blindness problem by building models that only blind us to problems that we would be foolish to choose. In practice, we can never know how to build such models. However, we can know how to build self-refining models for pursuing this end, which is the timeless end of deciding well.

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When applied sequentially an infinite number of times, a self-refining model of deciding well yields a perfectly refined model of deciding well. We may think of its end as a boundless end. Further, a self-refining process may break down this boundless end into the pursuits of facets of this boundless end. If it does, then these pursuits’ ends will also be boundless ends.

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In discussing boundless ends, we can avoid much confusion by capitalizing them. Using this convention, we may call the boundless end of deciding well WISDOM. We may define ‘religion’ to mean the pursuit of linking with something infinitely greater than ourselves and ‘theism’ to mean belief in the existence of a divine being or beings. If we do, the convention of capitalizing boundless ends has religious overtones that may or may not be theistic.

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In his book, DARWIN’S DANGEROUS IDEA: EVOLUTION AND THE MEANINGS OF LIFE, philosopher Daniel Dennett distinguished between normative belief systems based on science (“cranes”) and those based on miracles (“skyhooks”). The approach to deciding well taken in this book is a grand strategy for building an infinitely large crane. Taking this approach, we base our values on the knowledge we need to pursue Wisdom well. This grand strategy for learning does not call for us to abandon the study of traditional religious texts. It only calls for us to interpret these texts in the light of pursuing Wisdom.

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The Truth of Wisdom

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We learn ever more about pursuing the boundless end of deciding well (Wisdom) by pursuing the boundless end of believing well (Truth).

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Eighteenth-century philosopher David Hume provided us with a reason to believe that pursuing Truth is an endless process. This reason concerns the way we create general beliefs about the world from experience. Consider a claim that we might make about the color of marbles in an urn holding five hundred marbles. Suppose the first four hundred marbles that we randomly pull from the urn are white. We can reasonably believe that all the marbles in the urn are white. However, we cannot prove this belief without examining every marble in the urn.

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Philosophers call the problem of whether creating general beliefs from experience is reasonable THE PROBLEM OF INDUCTION. This problem raises doubts about the validity of such general beliefs as all crows are black, all ice cubes are cold, and the speed of light in space is the same for all inertial frames.

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Nineteenth-century philosopher John Stuart Mill provided us with a famous real-world example of this problem. Before the European exploration of Australia, Europeans believed that all swans are white. The European discovery of a new Australian bird species led Europeans to believe that all swans are either white or black. Mill intended his “black swan” example to show us how unexpected events may change our beliefs. The complete history also shows us how an unexpected event may change the concepts underlying our beliefs. Rather than including the new species in the swan genus (CYGNUS), Europeans first created a new genus (CHENOPIS). Under this genus, the new birds are as distant from swans as golden jackals are from domestic dogs. Europeans did not need to change their belief that all swans are white. It was only after they reclassified the new birds into the swan genus that they needed to change their belief about the color of swans. The problem with inductive reasoning concerns not only our beliefs but also the concepts underlying our beliefs. ALLOWING EXPERIENCE TO CHANGE OUR CONCEPTS BLURS THE DISTINCTION BETWEEN TRUTHS GROUNDED IN MEANINGS AND TRUTHS GROUNDED IN FACTS. Philosophers will recognize this as the analytic versus synthetic truth distinction, which is the first of W. V. O. Quine’s two dogmas of empiricism.

· · 

This more profound problem with inductive reasoning raises the issue of the usefulness of concepts. Imagine an isolated village in an undeveloped tropical country where the only water source is liquid water that falls from the sky. Given their limited experience with sources of water, the villagers use the term ‘rain’ to denote the concept of “the source of water that makes the ground wet.” Now imagine that these villagers migrate to a place where dew forms on cold, humid mornings. Confronted with a new cause of wet ground, they face a choice. They may choose to continue to use ‘rain’ to denote “the source of water that makes the ground wet.” Alternatively, they may choose to use ‘rain’ to denote “liquid water that falls from the sky.” This choice, in part, depends on how they use ‘rain’ in their daily lives. For example, if they use ‘rain’ in a rule that tells them when to plant their crops, failure to change either their rule or the meaning of ‘rain’ may cause them to plant their crops at the wrong time.

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From modern views of believing well, concepts’ usefulness raises sociological questions about how people collectively choose concepts. These questions include who chooses, why they choose as they do, and why other people accept what they choose. In contrast, from the view of believing well put forth in this work, concepts’ usefulness raises the question of what system of concepts best helps us pursue the boundless end of believing well (Truth). Addressing this question calls for us to choose a frame, which calls for us to choose a frame, which calls for us to choose a frame, and so on to infinity. We can address this framing problem well by pursuing the boundless end of deciding well (Wisdom), which is the problem we set out to address.

· · 

From a view that DOES NOT allow learning by doing, exploring the relations between pursuing Wisdom and pursuing Truth is fruitless. It simply leads us back to our original problem. However, from a view that DOES allow learning by doing, exploring the relations between pursuing Wisdom and pursuing Truth is fruitful. We learn that pursuing Wisdom well calls for us to pursue Truth well, and that pursuing Truth well calls for us to pursue Wisdom well.

· · 

We can use this insight into the virtuous circle of pursuing Truth and pursuing Wisdom to build self-refining models of deciding well. To refine the process of deciding well, we need to break it down into wieldier parts. We can do so by breaking it into the pursuits of universally useful knowledge resources that we can never have in excess. We may call these facets of Wisdom BOUNDLESS FACTORS OF DECIDING WELL. Taken together, the pursuits of these factors form FACETED models of deciding well. We can use these models to judge whether the problems we find “ring true” with all we currently know about deciding well. We may call this complex approach to deciding well THE BOUNDLESS APPROACH and the complex view of this approach THE BOUNDLESS VIEW.

· · · 

Steps for Building Boundless Models

· · 

We can build faceted models of deciding well for helping us find problems to solve by repeating five basic steps: (1) tentatively putting forth a member of the set of boundless factors of deciding well; (2) building a frame for pursuing this boundless factor by defining it and the best means of pursuing it in terms of each other; (3) adding what we currently believe we know about pursuing this facet of Wisdom to this bare frame; (4) adding this frame to a faceted model of deciding well; and (5) reconciling the resulting faceted model as best as we can given our current ignorance of not only the current state of the world but also all possible future states of the world.

· ·

The simplest model we can build using these five steps is the model in which the only member of the set of boundless factors is Wisdom. We build the frame for this simple model by defining Wisdom to be the boundless end of deciding well and by defining deciding well to be the best means of pursuing Wisdom. Defining these two key concepts in terms of each other creates holes in our belief systems. Chief among these holes is not knowing the precise definition of Wisdom. To make this model useful in finding these blind spots, we need to add frames to this single-frame model.

· · 

We can begin by adding a frame for believing well. We do so by defining Truth to be the boundless end of believing well and by defining believing well to be the best means of pursuing Truth. Pursuing Wisdom calls for us to pursue Truth, and pursuing Truth calls for us to pursue Wisdom. The better we pursue these two boundless ends, the more closely these pursuits intertwine. If we sought both perfectly, they would be the same pursuit.

· · 

We can use this faceted model to weed out problems that are not consistent with our current beliefs about deciding well and believing well. Viewing potential problems to solve from more than one frame gives us a better chance of avoiding the abstraction problems that arise from viewing the world from a single frame.

· · · 

Ever-More-Complete Boundless Models

· · 

We naturally seek to live well, to survive, thrive, and learn to survive and thrive ever more wisely. In terms of modern biology, the end of living well is TELEONOMIC, an end determined by internal programming. Adding the frame of living well provides us with another means of judging problems to solve. We can do so by defining the boundless end of living well to be HAPPINESS and living well to be the best means of pursuing Happiness. So conceived, Happiness is a boundless factor of deciding well.

· · 

Pursuing the boundless factors of deciding well also calls for us to live and work well with others, including people separated from us by great distances and long periods. The ancient Chinese provided us with a template for working together over great distances and long periods: THE DEBTS THAT WE OWE TO OUR ANCESTORS WE PAY TO OUR DESCENDANTS. Using this template, we can work well with others across great distances and long periods with the universal moral rule: THE DEBTS WE CANNOT PAY TO WHOM THEY ARE DUE WE PAY TO OTHERS BY DECIDING WELL. These debts include the debts that we owe to those who provided us with the knowledge we use freely. They also include the debts we owe the whole of life for the natural resources we need to live well. We may call the boundless end of living and working with others well JUSTICE. So conceived, Justice is a boundless factor of deciding well.

· · 

Pursuing any boundless factor well calls for us to decide well, which in turn calls for us to pursue all boundless factors well. Deciding well calls for us to fit our beliefs together based on this symmetric structure of deciding well. We may call the process of contemplating how best to remove waste from deciding well CONTEMPLATING WELL and the boundless end of contemplating well BEAUTY. So conceived, Beauty is a boundless factor of deciding well.

· · 

With each new boundless factor that we add to our faceted model of deciding well, we potentially understand better what it is to decide well. With better understanding, we can more readily judge whether we have found beautiful problems to solve. After finding what we believe is a beautiful problem to solve, we can use models that predict well within its domain to judge solutions to it. In taking the boundless approach to deciding well, we consider not only the supply but also the demand side of the market for tools for helping us decide well.

· · 

From the boundless view, the further we choose to “see” into the future, the less we believe we know relative to what we do not know. At the limit of imagination, all that we presume is True is the ideal set of basic rules for refining everyday thinking. In mathematical terms, members of this set are the axioms of the science of deciding well. For a 500-year-old depiction of a partially refined version of this ideal set of rules, see Appendix C (Renascent Art).

· · · · 

THE LEARNING AGE

· · 

We may think of this boundlessly reasonable strategy for learning ever more about deciding well as the virtuous circle of producing well and reasoning well in the natural pursuit of living well. The idea of the co-evolution of producing well and reasoning well is not new. In the 1930s, psychologist Alexander Luria studied the ability of illiterate peasants to reason well. Although logic dates at least as far back as Aristotle, these people were unable to use it. Given that all bears in the far north are white and that the Novaya Zemlya islands are in the far north, they could not reason why all bears living on these islands are white. They failed to reason LOGICALLY.

· ·

We can see something SIMILAR today. Historians like to reduce periods to simple themes, for example, the Agricultural Age and the Industrial Age. Although a reasonable concept of beauty dates back at least as far as Plato, the people who labeled the successor to the Industrial Age the Information Age failed to use it. They failed to reason BEAUTIFULLY.

· ·

The concept of “the Information Age” stems from Claude Shannon’s information theory, which concerns processing information well. In contrast, “the Learning Age” stems from Norbert Wiener’s science of goal-seeking (CYBERNETICS), which concerns seeking ends well. Everything else being equal, processing information well increases the pace of change, which increases the need for learning well. THE INFORMATION AGE IS BUT THE THRESHOLD OF THE LEARNING AGE.

· · · · ·

Next five chapters: REFINING EVERYDAY THINKING WELL

Albert Einstein quote: “The whole of [modern] science is nothing more than a refinement of everyday thinking. It is for this reason that the critical thinking of the physicist cannot be restricted to the examination of concepts of his own specific field. He cannot proceed without considering critically a much more difficult problem, the problem of analyzing the nature of everyday thinking.”

· ·

From the boundless view of this work, the whole of science is the process rather than the product of refining everyday thinking. This abridgment of BOUNDLESS REASON, A UNIVERSAL STRATEGY FOR DECIDING WELL does not include the second through sixth chapters, which help us to refine our understanding of living well, contemplating well, believing well, governing ourselves well, and linking well.

· · · · · 

Seventh Chapter: COMPETING WELL

Sun Wu quote: “Supreme excellence consists in breaking the enemy’s resistance without fighting.”

· · 

If we were perfectly wise, we would agree on which beliefs best help us decide well. Because we are not perfectly wise, we dispute beliefs of all kinds. We may call the process of settling disputes ever more wisely COMPETING WELL and the boundless end of this process WINNING.

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THE SCOPE OF GAME THEORY

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In the early 1980s, cognitive scientist Douglas Hofstadter sent a registered letter to twenty academics familiar with modern game theory. In this letter, he invited them to play a two-player game against each of nineteen colleagues. He told them that this was a one-time game, that the players were equally bright, and that they should aim at getting as much money as possible rather than being a “winner.” He also asked them not to discuss the game with anyone. Hofstadter described his simultaneous game as a two-person game. If both players cooperated, each would receive $3. If both players defected, each would receive $1. If one defected and the other cooperated, the defector would receive $5, and the cooperator would receive $0. Hence, if everyone cooperated, everyone would receive $57 (19 × $3); if everyone defected, everyone would receive $19 (19 × $1); and if eleven people cooperated and nine people defected, each cooperator would get $30 (10 × $3 + 9 × $0) and each defector would get $63 (11 × $5 + 8 × $1). In closing, Hofstadter asked each player to tell him whether and why they wished to cooperate or defect.

· · 

The results of this experiment were that six people chose to cooperate and fourteen chose to defect. Each of the cooperators received $15 (5 × $3 + 14 × $0) and each of the defectors received $43 (6 × $5 + 13 × $1). Both groups received less than the $57 each would have received had all players chosen to cooperate.

· · · 

A NORMAL ANOMALY

· · 

The players’ reactions to this game were as interesting as the results themselves. An expert in modern game theory saw no reason to cooperate. A biologist was so sure that no one would cooperate that he began his phone call by announcing: “Okay, Hofstadter, give me the $19.” A physicist reported that he wanted to cooperate but said that he could not find any way of justifying it. Another player became so frustrated that he ended up flipping a coin to determine whether to cooperate or defect. Former “Mathematical Games” columnist Martin Gardner claimed that he did not know how to behave rationally:

“Horrible dilemma. I really don’t know what to do about it. If I wanted to maximize my money, I would choose to D [defect] and expect that others would also; to maximize satisfaction, I’d choose C [cooperate], and hope other people would do the same (by the Kantian imperative). I don’t know, though, how one should behave RATIONALLY. You get into endless regresses: ‘If they all do X, then I should do Y, but then they’ll anticipate that and do Z, and so...’ You get trapped in an endless whirlpool.”

· · 

Students of Thomas Kuhn may recognize these reactions as typical responses to stimuli that do not fit current theoretical models. Kuhn compared these responses to subjects’ reactions in a famous 1949 experiment by psychologists Jerome Bruner and Leo Postman. In this experiment, researchers told subjects that the experiment tested how quickly people could recognize playing cards. They did not tell subjects that some of these cards had the color of the suit reversed. The researchers began flashing these cards quickly but gradually slowed down. Kuhn wrote of this experiment:

“Even on the shortest exposures many subjects identified most of the cards, and after a small increase all the subjects identified them all. For the normal cards these identifications were usually correct, but the anomalous cards were almost always identified, without apparent hesitation or puzzlement, as normal. The black four of hearts might, for example, be identified as the four of either spades or hearts. Without any awareness of trouble, it was immediately fitted to one of the conceptual categories prepared by prior experience. One would not even like to say that the subjects had seen something different from what they identified. With a further increase of exposure to the anomalous cards, subjects did begin to hesitate and to display awareness of an anomaly. Exposed, for example, to the red six of spades, some would say: That’s the six of spades, but there’s something wrong with it—the black has a red border. Further increase of exposure resulted in still more hesitation and confusion until finally, and sometimes quite suddenly, most subjects would produce the correct identification without hesitation. Moreover, after doing this with two or three of the anomalous cards, they would have little further difficulty with the others. A few subjects, however, were never able to make the requisite adjustment of their categories. Even at forty times the average exposure required to recognize normal cards for what they were, more than 10 percent of the anomalous cards were not correctly identified. And the subjects who then failed often experienced acute personal distress. One of them exclaimed: ‘I can’t make the suit out, whatever it is. It didn’t even look like a card that time. I don’t know what color it is now or whether it’s a spade or a heart. I’m not even sure now what a spade looks like. My God!’”

· · 

Modern game theory is the mathematical study of BOUNDED strategic situations. Theorists build models that restrict learning by doing. One way they do this is to assume that the situation concerns a game that occurs once. This way excludes all learning by doing. Another way is by building models based on identical games that recur. This excludes all learning except that from playing identical recurring games. In contrast, Hofstadter created a game in which there are identical games that occur once. Just as a red queen of spades does not fit neatly into a standard deck of cards, his game did not fit neatly into early 1980s game theory.

· · · 

AN EXTRAORDINARY ANOMALY

· · 

Hofstadter believed that the solution to the anomaly he discovered was for people to look for symmetries that might serve as common ground for cooperating well. He imagined that somewhere in the universe there exist SUPERRATIONAL SOCIETIES, societies in which people seek to find such ground. THE REASON OF MODERN GAME THEORY—LOGIC—IS THE WRONG FORM OF REASON FOR HELPING US FIND PROBLEMS TO SOLVE IN WORKING TOGETHER WELL.

· · 

From the boundless view, Hofstadter was right to have people look for common ground based on symmetry but failed to find it in the timeless symmetry of the science of deciding well. Defectors in the “game” of deciding well are people who believe that they can separate their problems from the universal problem of deciding well.

· · 

In 1931, Kurt Gödel used self-reference and a clever way of numbering mathematical statements to prove that no set of axioms (basic rules) can prove all true statements in the arithmetic of natural numbers without creating logical inconsistencies. Game theory pioneer John von Neumann was the first person to grasp the importance of this work. Given that the set of rules for reasoning well in science includes the set of rules for reasoning well in mathematics, no set of rules for reasoning well in science can be both logically consistent and complete.

· · 

In early 1955, von Neumann agreed to give the 1956 Silliman Memorial lectures at Yale. He chose as his topic the mathematics of reason. Fellow polymath and former colleague Jacob Bronowski described what he sought as “a procedure, as a grand overall way of life—what in the humanities we would call a system of values.” Implicit in Bronowski’s description of what von Neumann sought is the idea of the game of deciding well.

· · 

In addition to being a pioneer in game theory, von Neumann was a pioneer in cybernetics (the science of goal-seeking). He contributed to both FIRST-ORDER cybernetics (seeking well-defined ends, e.g., missile guidance) and HIGHER-ORDER cybernetics (seeking ill-defined ends, e.g., biological fitness). In seeking the mathematics of reason, he sought the rules underlying the whole of science. Regrettably, he died before completing this work.

. .

From the boundless view, he sought the reason of INFINITE-ORDER cybernetics, of seeking the timeless end of deciding well by breaking this end into timeless factors. In terms of the boundless view, he sought boundless reason.

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Boundless reason may lead to artificial beings that learn to decide ever more wisely several orders of magnitude faster than human beings. We would be wise to cooperate with these potential people by creating a culture they would want to join, a culture based on boundless reason. Given recent progress in artificial intelligence, we ought to begin directly.

· · · · 

THE SCOPE OF STRATEGY

· · 

To compete well, we need to consider the spatial boundaries that define the field. For example, at the Battle of Thermopylae, boundaries defined by the Athenian-controlled Gulf of Malia and the shoreline cliffs protected the Spartans and their allies from attacks from the north. To compete well, we also need to consider the temporal boundaries that define the field. In the second half of the twentieth century, the most important development in strategic thinking was the idea of deciding well more quickly than competitors do. In terms of boundaries, this concerns getting closer than competitors to the boundary formed by the flow of time. The person most responsible for the popularity of this idea was John Boyd.

· · · 

E–M THEORY

· · 

John Boyd was an officer in the United States Air Force. After a tour as an F-86 Sabre fighter pilot in the Korean War’s closing months, the Air Force assigned Boyd to Nellis Air Force Base for further instruction. His skills were such that he stayed on as an instructor at the Fighter Weapons School. Before leaving this post, he wrote the first manual on combat tactics for jet aircraft, which eventually became a textbook for air forces worldwide. The Air Force then sent him back to college. While studying for an exam in thermodynamics, he had the insight to describe close-in aerial combat in energy terms. He later worked with mathematician Thomas Christie to refine this idea into what he called Energy–Maneuverability (E–M) theory.

· · 

E–M theory revolutionized not only close aerial combat but also fighter aircraft design. Boyd used it to demonstrate the weaknesses of American fighter planes in close aerial combat. His analysis led the Air Force to assign him to a design program for a massive swing-wing fighter. He predicted that this plane would be as big a disaster as the naval version of the F111. In its place, he proposed a lightweight fixed-wing fighter. The Air Force decided on a large fixed-wing fighter, the F-15 Eagle. Boyd believed that the F-15 was too expensive to deploy in sufficient numbers. With fellow defense reformers’ help, Boyd convinced enough people within the military-industrial-congressional complex to develop two lightweight fighter prototypes, the YF-16 and YF-17. These reformers were then able to force the Air Force to buy the YF-16. During the development process, the Air Force changed the YF-16 from an inexpensive dogfighter into a more expensive multirole fighter, the F-16 Fighting Falcon (aka Viper). The Navy eventually purchased a larger and even more expensive multirole fighter based on the YF-17 design, the F/A-18 Hornet.

· · · 

OODA LOOP ANALYSIS

After retiring from the Air Force in 1975, Boyd envisioned deciding well as a self-similar process based on a recurring sequence of steps, a decision cycle. In his decision cycle, we observe the world, orient ourselves in the world, decide on a course of action, and act. He called this observe–orient–decide–act cycle an OODA LOOP.

· · 

Boyd first used his OODA loop model to address the tactical issue of why F-86 pilots were as successful against MiG-15 pilots as they were. He concluded that they overcame the E–M weaknesses of their airplanes by using knowledge that allowed them to decide faster than their opponents. This knowledge was in the form of bubble canopies, g-suits, hydraulic controls, and better tactical rules. It was also in the form of better habits, which Boyd described using the German term ‘Fingerspitzengefühl’ (“feeling in the fingertips”). Deciding faster allowed F-86 pilots to “get inside the decision cycles” of their rivals, where they could remain relatively safe until their opponents made an exploitable mistake. It also gave them more time to consider how best to force their opponents to make mistakes.

· · 

He next applied his OODA loop model to strategic problems. He recognized that the greater the scope of the problems people choose to address, the more important what they may learn becomes relative to what they currently know. To address the issue of knowing what people ought to learn, Boyd added a learning function to his orientation step. He labeled what we ought to learn INGREDIENTS (of the next cycle):

INSIGHT: Ability to peer into and discern the inner nature or workings of things.

·  

INITIATIVE: Internal drive to think and take action without being urged.

·  

ADAPTABILITY: Power to adjust or change in order to cope with new or unforeseen circumstances.

·  

HARMONY: Power to perceive or create interaction of apparently disconnected events or entities in a connected way.

· · 

Boyd used this insight into what we ought to learn to refine his beliefs about how best to organize problems into a hierarchy:

NATIONAL GOAL: Improve our fitness, as an organic whole, to shape and cope with an ever-changing environment.

·  

GRAND STRATEGY: Shape pursuit of national goal so that we not only amplify our spirit and strength (while undermining and isolating our adversaries) but also influence the uncommitted or potential adversaries so that they are drawn toward our philosophy and are empathetic toward our success.

·  

STRATEGIC AIM: Diminish adversary’s capacity while improving our capacity to adapt as an organic whole, so that our adversary cannot cope—while we can cope—with events/efforts as they unfold.

·  

STRATEGY: Penetrate adversary’s moral–mental–physical being to dissolve his moral fiber, disorient his mental images, disrupt his operations, and overload his system, as well as subvert, shatter, seize, or otherwise subdue those moral–mental–physical bastions, connections, or activities that he depends upon, in order to destroy internal harmony, produce paralysis, and collapse adversary’s will to resist.

·  

GRAND TACTICS: Operate inside adversary’s observation–orientation–decision–action loops, or get inside his mind–time–space, to create tangles of threatening and/or non-threatening events/efforts as well as repeatedly generate mismatches between those events/efforts adversary observes, or imagines, and those he must react to, to survive;

thereby

Enmesh adversary in an amorphous, menacing, and unpredictable world of uncertainty, doubt, mistrust, confusion, disorder, fear, panic, chaos … and/or fold adversary back inside himself;

thereby

Maneuver adversary beyond his moral–mental–physical capacity to adapt or endure so that he can neither divine our intentions nor focus his efforts to cope with the unfolding strategic design or related decisive strokes as they penetrate, splinter, isolate or envelop, and overwhelm him.

·  

TACTICS: Observe–orient–decide–act more inconspicuously, more quickly, and with more irregularity as basis to keep or gain initiative as well as shape and shift main effort: to repeatedly and unexpectedly penetrate vulnerabilities and weaknesses exposed by that effort or other effort(s) that tie-up, divert, or drain-away adversary attention (and strength) elsewhere.

· · 

Boyd used this hierarchy in shaping the strategy for Operation Desert Storm, the 1991 campaign to remove Iraqi troops from Kuwait. This strategy focused on breaking down the enemy’s “moral–mental–physical capacity to adapt or endure.” It included purposely creating in Saddam Hussein and his top leadership the dissonance experienced by the subjects of Bruner and Postman’s playing card experiment. United States Marine Corps Commandant Charles Krulak wrote of Boyd’s contribution: “The Iraqi army collapsed morally and intellectually under the onslaught of American and Coalition forces. John Boyd was an architect of that victory as surely as if he’d commanded a fighter wing or a maneuver division in the desert. His thinking, his theories, his larger than life influence, were there with us in Desert Storm.”

· · · 

THE GRANDEST POSSIBLE STRATEGY

Boyd based his hierarchy on the modern belief that nations ought to seek to “improve our fitness, as an organic whole, to shape and cope with an ever-changing environment.” Nationalism bounds his thinking.

· · 

To compete well, we need to consider moral as well as spatial and temporal boundaries. In taking higher moral ground, people tend to be more successful in “attracting the uncommitted, in magnifying their own spirit and strength, and in undermining the dedication and determination of their adversaries.”

· · 

From the boundless view, the highest moral ground is the grand strategy for deciding well described in this work. In light of the constraints we face in knowing the right, this strategy calls for keeping Abraham Lincoln’s faith that right makes might.

· · · · 

THE SCOPE OF EVOLUTION

· · 

From the prevailing modern view, living beings who always seek to cooperate in pursuing the timeless end of deciding well before they seek to compete are anomalies. Our national goal of improving our fitness to cope with and shape our environment is superior to our grand strategy. To prevent logical contradictions that arise from self-reference, we do not apply the process of refining everyday thinking to itself. WE EXCLUDE REFINING EVERYDAY THINKING FROM REFINING EVERYDAY THINKING.

· · 

From the boundless view, living beings who always seek to compete before they seek to cooperate in pursuing the timeless end of deciding well are a special case of beings that have not yet developed the wisdom to do otherwise. Our national goal is to decide well using the boundless approach. We recognize that the process of refining everyday thinking applies to itself. We also recognize that what happens to us may change how our genes and our future descendants’ genes work. Most importantly, we recognize that the need to remove waste from living well exists from the smallest organism to the largest organization.

· · 

As people, we ought to explain the world in ways that ring truest with all that we currently know about deciding well. Hence, we ought to take the boundless view of biological evolution, which calls for explaining the world based on learning to decide ever more wisely.

· · · · · 

Eighth Chapter: REASONING WELL

Alfred North Whitehead quote: “The function of reason is to promote the art of life.”

· ·  

Using knowledge resources does not use them up. From this emerges a natural tendency for living beings to create knowledge resources useful in the natural pursuit of living well. In doing so, some living beings create language. Language greatly speeds the process of replacing scarce non-knowledge with knowledge resources in living well. The capacity to use language is what distinguishes people from other living beings.

· · 

Over time, some people discover or invent rules for relating beliefs well. We may call these RULES OF REASON. From modern views, sets of rules of reason naturally fall into two major categories. The first contains rules that people use to relate beliefs based on received (given/settled) concepts. We may call these RULES OF LOGIC after the rules that Aristotle used to relate beliefs well. The second contains rules that concern how people invent or discover concepts. We may call these RULES OF MODERN DIALECTICS after the means that Plato used to seek ideal concepts for living well.

· · 

From the boundless view, these two sets of rules are incomplete. The complete set also includes rules for using rules of logic and rules of modern dialectics well. We may call this complete set of rules for deciding well RULES OF BOUNDLESS REASON.

· · 

We can easily see the incompleteness of the rules of logic. Consider the claim, “This is the best frame for deciding well.” We can never prove this statement logically. The best we can do is to disprove it empirically by finding a better frame. PEOPLE WHO BELIEVE THAT REASON IS NOTHING MORE THAN LOGIC TAKE AN ENGINEERING APPROACH TO OVERCOMING CONSTRAINTS IN DECIDING WELL.

· · 

We can also see the incompleteness of the rules of logic combined with those of modern dialectics. This combination does not encompass all possible rules for slowing the creation of embacles (pent-up stress) without slowing progress. Embacles hinder progress and increase turbulence (uneven flow), particularly turbulence in the form of debacles (the sudden release of large amounts of pent-up stress). PEOPLE WHO BELIEVE THAT REASON IS NOTHING MORE THAN LOGIC AND MODERN DIALECTICS TAKE A MODERN EVOLUTIONARY APPROACH TO OVERCOMING CONSTRAINTS IN DECIDING WELL.

· · 

In contrast, the set of rules of boundless reason includes all possible rules for deciding well, which includes rules for using rules of logic and rules of modern dialectics well. Using these rules speeds progress by helping to create knowledge useful in deciding well, which includes knowledge useful in adapting to change. Such knowledge improves the trade-off between progress and turbulence. Imagine the traffic dynamics of self-driving vehicles that collectively learn from their mistakes. As vehicles learn, the rate at which traffic flow becomes turbulent rises.

· · · · 

TESTING BOUNDLESS REASON

· · 

As we saw in the fifth chapter, we may test boundless reason empirically by basing our sovereign rights and responsibilities on it. Everything else being equal, the flourishing of nations having governments based on boundless sovereign rights stories supports the usefulness of boundless reason. So too does such governments’ ability to attract both imitators and people who decide well.

· · 

We may also test empirically whether boundless reason is natural. For example, we may test whether changes in financial asset prices are random. From the view of modern financial economics, they are: There is no direction to cultural evolution. From the boundless view, they are not: The self-similar process of deciding well creates turbulence in the flow of economic resources. The distributions of financial asset price changes are more like those of human wealth and income than those of human height and weight.

· · · · 

GOVERNING OUR MINDS WELL

· · 

Missing from the boundless approach to reasoning well thus far is a means of understanding constraints in our ability to receive and process information unconsciously. To decide well, we need to consider these constraints. Both as people and as people living with people, we need to consider our unconscious minds.

· · 

We may call the unconscious parts of our minds DAEMONS after the term computer scientists use to describe processes that run in the background. We can learn to program some of these daemons. For example, we can program our craving for certain foods. The more we learn about why we crave foods, the easier it is to program ourselves to crave healthy foods. Similarly, we can program what makes things ring true to us. The more we know why things ring true, the easier it is to program ourselves to know what is truly good for us.

· · 

To govern our minds well, the activities of various parts of our minds have somehow to work together in deciding well. In the words of Jacob Bronowski, they have somehow to be “interlocked and made to match so that we devise a plan, a procedure, as a grand overall way of life—what in the humanities we would call a system of values.”

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Conflicts between parts of our minds call for us to govern our minds well. Following in the holistic tradition of Plato, we may call the boundless end of governing our minds well EUDAEMONIA. So conceived, Eudaemonia is a boundless factor of deciding well.

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Given our ignorance of deciding well, the pursuits of Happiness and Eudaemonia are not the same. Opening our daemons to consciousness may result in opening consciousness to our daemons. Genius may lead to madness. We can see this in the sad ends of Georg Cantor and Kurt Gödel. The daemons they believed to be perfectly good would not let them give up searching for logically consistent and complete solutions to open-ended problems. Cantor died in an asylum. Gödel starved himself to death. A grand strategy for deciding well that does not include governing our minds well is dangerously incomplete.

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REUNIFYING THE REASON OF PLATO AND ARISTOTLE

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In Greco-Abrahamic thought, the relation between governing our minds well and the science of deciding well is an ancient one. In Plato’s CHARMIDES, an early dialogue that concerns soundness of mind and excellence in character (SOPHROSYNE), Plato suggested that we might best acquire this virtue using the science of science. However, he had two concerns. First, the science of science calls for us to know what we do not know. Plato wondered whether this is a contradiction. Second, he wondered whether the science of science is too idealistic to be useful.

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In THE REPUBLIC, Plato tried to solve both problems by using the metaphor of governing city-states well for governing our minds well. His program for governing city-states well called for an idealistic system for breeding, educating, and empowering people capable of governing city-states well. In his last and longest dialogue, LAWS, he explored more pragmatic means of governing city-states well. His most famous student, Aristotle, furthered this more pragmatic approach to living well by separating first principles from beliefs based on first principles (metaphysics from physics) and by professing rules of logic. As we have seen throughout this work, a major problem with this reductionist approach to believing well is that it tends to blind us to better problems to solve. The science of deciding well put forth in this work addresses this bounds-blindness problem.

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CONCLUSION

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In putting our faith in the science of deciding well, we put our faith in freedom, cooperation, and self-interest enlightened by boundless reason. We put little faith in people who pretend to have knowledge that surpasses what anyone can currently know, especially in people who would be philosopher kings. In the words of Jacob Bronowski: “If we are anything, we must be a democracy of the intellect. We must not perish by the distance between people and government, between people and power, by which Babylon and Egypt and Rome failed. And that distance can only be conflated, can only be closed, if knowledge sits in the homes and heads of people with no ambition to control others, and not up in the isolated seats of power.”

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END OF RSVP ABRIDGMENT OF BOUNDLESS REASON, A UNIVERSAL STRATEGY FOR DECIDING WELL

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