b Can a Company be pro-regulation and pro-commerce? Gregg Renfrew from Beautycounter thinks so By brandleadership.wordpress.com Published On :: Fri, 19 Feb 2016 18:59:09 +0000 It’s the middle of an election year and, according to the Pew Research Center, the country hasn’t been this polarized since the Civil War. In such a climate, it would seem to be an oxymoron for a company to push for both financial growth and tighter regulations. Gregg Renfrew, CEO & Founder of Beautycounter, wouldn’t […] Full Article *Gabriela Torres Patiño Brand Strategy Business Values Customer Experience Event Marketing
b Reflections on Business, Leadership, and Branding: Shelly Lazarus ’70 By brandleadership.wordpress.com Published On :: Mon, 22 Feb 2016 15:20:02 +0000 Much has changed in the world of advertising from the picture painted by Mad Men. Shelly Lazarus ’70, Chairman Emeritus, Ogilvy & Mather, was one of the women helping pioneer these changes. Making the journey from ‘the only woman in the room’ to CEO and Chairman of Ogilvy gives Lazarus a lot to reflect on […] Full Article *Matthew Quint Brand Strategy Business Values Leadership Uncategorized BRITE Conference Ogilvy & Mather Shelly Lazarus
b Musculoskeletal Complications of Diabetes Mellitus By clinical.diabetesjournals.org Published On :: 2001-07-01 Rachel Peterson KimJul 1, 2001; 19:Practical Pointers Full Article
b Evaluating the Effect of U-500 Insulin Therapy on Glycemic Control in Veterans With Type 2 Diabetes By clinical.diabetesjournals.org Published On :: 2015-01-01 Joseph A. GranataJan 1, 2015; 33:14-19Feature Articles Full Article
b Cutaneous Manifestations of Diabetes Mellitus By clinical.diabetesjournals.org Published On :: 2015-01-01 Michelle DuffJan 1, 2015; 33:40-48Practical Pointers Full Article
b Gestational Diabetes in High-Risk Populations By clinical.diabetesjournals.org Published On :: 2013-04-01 Wilfred FujimotoApr 1, 2013; 31:90-94Diabetes Advocacy Full Article
b The Diabetes Attitudes, Wishes and Needs Second Study By clinical.diabetesjournals.org Published On :: 2015-01-01 Martha M. FunnellJan 1, 2015; 33:32-36Translating Research to Practice Full Article
b Case Study: Potential Pitfalls of Using Hemoglobin A1c as the Sole Measure of Glycemic Control By clinical.diabetesjournals.org Published On :: 2004-07-01 Huy A. TranJul 1, 2004; 22:141-143Case Studies Full Article
b Effects of Glycemic Control on Diabetes Complications and on the Prevention of Diabetes By clinical.diabetesjournals.org Published On :: 2004-10-01 Jay S. SkylerOct 1, 2004; 22:162-166Feature Articles Full Article
b Diabetes and Periodontal Infection: Making the Connection By clinical.diabetesjournals.org Published On :: 2005-10-01 Janet H. SoutherlandOct 1, 2005; 23:171-178Feature Articles Full Article
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b Diabetes and Back Pain: Markers of Diabetes Disease Progression Are Associated With Chronic Back Pain By clinical.diabetesjournals.org Published On :: 2017-07-01 Lorenzo RinaldoJul 1, 2017; 35:126-131Feature Articles Full Article
b Diabetes Self-management Education and Support in Type 2 Diabetes: A Joint Position Statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics By clinical.diabetesjournals.org Published On :: 2016-04-01 Margaret A. PowersApr 1, 2016; 34:70-80Position Statements Full Article
b Integration of Clinical Psychology in the Comprehensive Diabetes Care Team By clinical.diabetesjournals.org Published On :: 2004-07-01 Steven B. LeichterJul 1, 2004; 22:129-131The Business of Diabetes Full Article
b The Potential of Group Visits in Diabetes Care By clinical.diabetesjournals.org Published On :: 2008-04-01 Andrew M. DavisApr 1, 2008; 26:58-62Feature Articles Full Article
b Clarifying the Role of Insulin in Type 2 Diabetes Management By clinical.diabetesjournals.org Published On :: 2003-01-01 John R. WhiteJan 1, 2003; 21:Feature Articles Full Article
b Gestational Diabetes Mellitus By clinical.diabetesjournals.org Published On :: 2005-01-01 Tracy L. SetjiJan 1, 2005; 23:17-24Feature Articles Full Article
b Therapeutic Inertia is a Problem for All of Us By clinical.diabetesjournals.org Published On :: 2019-04-01 Stephen BruntonApr 1, 2019; 37:105-106Editorials Full Article
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b SGLT-2 Inhibitors: A New Mechanism for Glycemic Control By clinical.diabetesjournals.org Published On :: 2014-01-01 Edward C. ChaoJan 1, 2014; 32:4-11Feature Articles Full Article
b Self-Monitoring of Blood Glucose: The Basics By clinical.diabetesjournals.org Published On :: 2002-01-01 Evan M. BenjaminJan 1, 2002; 20:Practical Pointers Full Article
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b Opportunities and Challenges for Biosimilars: What's on the Horizon in the Global Insulin Market? By clinical.diabetesjournals.org Published On :: 2012-10-01 Lisa S. RotensteinOct 1, 2012; 30:138-150Features Full Article
b Diabetes Management Issues for Patients With Chronic Kidney Disease By clinical.diabetesjournals.org Published On :: 2007-07-01 Kerri L. CavanaughJul 1, 2007; 25:90-97Feature Articles Full Article
b Health Care Transition in Adolescents and Young Adults With Diabetes By clinical.diabetesjournals.org Published On :: 2010-06-01 Michael E. BowenJun 1, 2010; 28:99-106Feature Articles Full Article
b Stigma in People With Type 1 or Type 2 Diabetes By clinical.diabetesjournals.org Published On :: 2017-01-01 Nancy F. LiuJan 1, 2017; 35:27-34Feature Articles Full Article
b Management of Diabetic Peripheral Neuropathy By clinical.diabetesjournals.org Published On :: 2005-01-01 Andrew J.M. BoultonJan 1, 2005; 23:9-15Feature Articles Full Article
b Building Therapeutic Relationships: Choosing Words That Put People First By clinical.diabetesjournals.org Published On :: 2017-01-01 Jane K. DickinsonJan 1, 2017; 35:51-54Commentary Full Article
b Application of Adult-Learning Principles to Patient Instructions: A Usability Study for an Exenatide Once-Weekly Injection Device By clinical.diabetesjournals.org Published On :: 2010-09-01 Gayle LorenziSep 1, 2010; 28:157-162Bridges to Excellence Full Article
b Engaging Patients in Education for Self-Management in an Accountable Care Environment By clinical.diabetesjournals.org Published On :: 2011-07-01 Christine A. BeebeJul 1, 2011; 29:123-126Practical Pointers Full Article
b Helping Patients Make and Sustain Healthy Changes: A Brief Introduction to Motivational Interviewing in Clinical Diabetes Care By clinical.diabetesjournals.org Published On :: 2008-10-01 Michele HeislerOct 1, 2008; 26:161-165Practical Pointers Full Article
b Diabetes Self-Management in a Community Health Center: Improving Health Behaviors and Clinical Outcomes for Underserved Patients By clinical.diabetesjournals.org Published On :: 2008-01-01 Daren AndersonJan 1, 2008; 26:22-27Bridges to Excellence Full Article
b Hypoglycemia in Type 1 and Type 2 Diabetes: Physiology, Pathophysiology, and Management By clinical.diabetesjournals.org Published On :: 2006-07-01 Vanessa J. BriscoeJul 1, 2006; 24:115-121Feature Articles Full Article
b Standards of Medical Care in Diabetes--2019 Abridged for Primary Care Providers By clinical.diabetesjournals.org Published On :: 2019-01-01 American Diabetes AssociationJan 1, 2019; 37:11-34Position Statements Full Article
b Perspectives in Gestational Diabetes Mellitus: A Review of Screening, Diagnosis, and Treatment By clinical.diabetesjournals.org Published On :: 2007-04-01 Jennifer M. PerkinsApr 1, 2007; 25:57-62Feature Articles Full Article
b Amylin Replacement With Pramlintide in Type 1 and Type 2 Diabetes: A Physiological Approach to Overcome Barriers With Insulin Therapy By clinical.diabetesjournals.org Published On :: 2002-07-01 John B. BuseJul 1, 2002; 20:Feature Articles Full Article
b The Disparate Impact of Diabetes on Racial/Ethnic Minority Populations By clinical.diabetesjournals.org Published On :: 2012-07-01 Edward A. ChowJul 1, 2012; 30:130-133Diabetes Advocacy Full Article
b Standards of Medical Care in Diabetes--2016 Abridged for Primary Care Providers By clinical.diabetesjournals.org Published On :: 2016-01-01 American Diabetes AssociationJan 1, 2016; 34:3-21Position Statements Full Article
b What's So Tough About Taking Insulin? Addressing the Problem of Psychological Insulin Resistance in Type 2 Diabetes By clinical.diabetesjournals.org Published On :: 2004-07-01 William H. PolonskyJul 1, 2004; 22:147-150Practical Pointers Full Article
b Standards of Medical Care in Diabetes--2018 Abridged for Primary Care Providers By clinical.diabetesjournals.org Published On :: 2018-01-01 American Diabetes AssociationJan 1, 2018; 36:14-37Position Statements Full Article
b Standards of Medical Care in Diabetes--2017 Abridged for Primary Care Providers By clinical.diabetesjournals.org Published On :: 2017-01-01 American Diabetes AssociationJan 1, 2017; 35:5-26Position Statements Full Article
b Standards of Medical Care in Diabetes--2015 Abridged for Primary Care Providers By clinical.diabetesjournals.org Published On :: 2015-04-01 American Diabetes AssociationApr 1, 2015; 33:97-111Position Statements Full Article
b Empowerment and Self-Management of Diabetes By clinical.diabetesjournals.org Published On :: 2004-07-01 Martha M. FunnellJul 1, 2004; 22:123-127Feature Articles Full Article
b Microvascular and Macrovascular Complications of Diabetes By clinical.diabetesjournals.org Published On :: 2008-04-01 Michael J. FowlerApr 1, 2008; 26:77-82Diabetes Foundation Full Article
b Heroism Science: Call for Papers, Special Issue: The Heroism of Whistleblowers By blog.richmond.edu Published On :: Tue, 21 Apr 2020 17:11:14 +0000 Heroism Science: Call for Papers, Special Issue The Heroism of Whistleblowers Edited by Ari Kohen, Brian Riches, and Matt Langdon Whistleblowers speak up with “concerns or information about wrongdoing inside organizations and institutions.” As such, whistleblowing “can be one of the most important and difficult forms of heroism in modern society” (Brown, 2016 p. 1). … Continue reading Heroism Science: Call for Papers, Special Issue: The Heroism of Whistleblowers → Full Article Activist Heroes
b Baseball and Linguistic Uncertainty By decisions-and-info-gaps.blogspot.com Published On :: Sun, 21 Aug 2011 14:00:00 +0000 In my youth I played an inordinate amount of baseball, collected baseball cards, and idolized baseball players. I've outgrown all that but when I'm in the States during baseball season I do enjoy watching a few innings on the TV.So I was watching a baseball game recently and the commentator was talking about the art of pitching. Throwing a baseball, he said, is like shooting a shotgun. You get a spray. As a pitcher, you have to know your spray. You learn to control it, but you know that it is there. The ball won't always go where you want it. And furthermore, where you want the ball depends on the batter's style and strategy, which vary from pitch to pitch for every batter.That's baseball talk, but it stuck in my mind. Baseball pitchers must manage uncertainty! And it is not enough to reduce it and hope for the best. Suppose you want to throw a strike. It's not a good strategy to aim directly at, say, the lower outside corner of the strike zone, because of the spray of the ball's path and because the batter's stance can shift. Especially if the spray is skewed down and out, you'll want to move up and in a bit.This is all very similar to the ambiguity of human speech when we pitch words at each other. Words don't have precise meanings; meanings spread out like the pitcher's spray. If we want to communicate precisely we need to be aware of this uncertainty, and manage it, taking account of the listener's propensities.Take the word "liberal" as it is used in political discussion.For many decades, "liberals" have tended to support high taxes to provide generous welfare, public medical insurance, and low-cost housing. They advocate liberal (meaning magnanimous or abundant) government involvement for the citizens' benefit.A "liberal" might also be someone who is open-minded and tolerant, who is not strict in applying rules to other people, or even to him or herself. Such a person might be called "liberal" (meaning advocating individual rights) for opposing extensive government involvement in private decisions. For instance, liberals (in this second sense) might oppose high taxes since they reduce individuals' ability to make independent choices. As another example, John Stuart Mill opposed laws which restricted the rights of women to work (at night, for instance), even though these laws were intended to promote the welfare of women. Women, insisted Mill, are intelligent adults and can judge for themselves what is good for them.Returning to the first meaning of "liberal" mentioned above, people of that strain may support restrictions of trade to countries which ignore the health and safety of workers. The other type of "liberal" might tend to support unrestricted trade.Sending out words and pitching baseballs are both like shooting a shotgun: meanings (and baseballs) spray out. You must know what meaning you wish to convey, and what other meanings the word can have. The choice of the word, and the crafting of its context, must manage the uncertainty of where the word will land in the listener's mind.Let's go back to baseball again.If there were no uncertainty in the pitcher's pitch and the batter's swing, then baseball would be a dreadfully boring game. If the batter knows exactly where and when the ball will arrive, and can completely control the bat, then every swing will be a homer. Or conversely, if the pitcher always knows exactly how the batter will swing, and if each throw is perfectly controlled, then every batter will strike out. But which is it? Whose certainty dominates? The batter's or the pitcher's? It can't be both. There is some deep philosophical problem here. Clearly there cannot be complete certainty in a world which has some element of free will, or surprise, or discovery. This is not just a tautology, a necessary result of what we mean by "uncertainty" and "surprise". It is an implication of limited human knowledge. Uncertainty - which makes baseball and life interesting - is inevitable in the human world.How does this carry over to human speech?It is said of the Wright brothers that they thought so synergistically that one brother could finish an idea or sentence begun by the other. If there is no uncertainty in what I am going to say, then you will be bored with my conversation, or at least, you won't learn anything from me. It is because you don't know what I mean by, for instance, "robustness", that my speech on this topic is enlightening (and maybe interesting). And it is because you disagree with me about what robustness means (and you tell me so), that I can perhaps extend my own understanding.So, uncertainty is inevitable in a world that is rich enough to have surprise or free will. Furthermore, this uncertainty leads to a process - through speech - of discovery and new understanding. Uncertainty, and the use of language, leads to discovery.Isn't baseball an interesting game? Full Article
b Robustness and Locke's Wingless Gentleman By decisions-and-info-gaps.blogspot.com Published On :: Tue, 20 Sep 2011 07:49:00 +0000 Our ancestors have made decisions under uncertainty ever since they had to stand and fight or run away, eat this root or that berry, sleep in this cave or under that bush. Our species is distinguished by the extent of deliberate thought preceding decision. Nonetheless, the ability to decide in the face of the unknown was born from primal necessity. Betting is one of the oldest ways of deciding under uncertainty. But you bet you that 'bet' is a subtler concept than one might think.We all know what it means to make a bet, but just to make sure let's quote the Oxford English Dictionary: "To stake or wager (a sum of money, etc.) in support of an affirmation or on the issue of a forecast." The word has been around for quite a while. Shakespeare used the verb in 1600: "Iohn a Gaunt loued him well, and betted much money on his head." (Henry IV, Pt. 2 iii. ii. 44). Drayton used the noun in 1627 (and he wasn't the first): "For a long while it was an euen bet ... Whether proud Warwick, or the Queene should win."An even bet is a 50-50 chance, an equal probability of each outcome. But betting is not always a matter of chance. Sometimes the meaning is just the opposite. According to the OED 'You bet' or 'You bet you' are slang expressions meaning 'be assured, certainly'. For instance: "'Can you handle this outfit?' 'You bet,' said the scout." (D.L.Sayers, Lord Peter Views Body, iv. 68). Mark Twain wrote "'I'll get you there on time' - and you bet you he did, too." (Roughing It, xx. 152).So 'bet' is one of those words whose meaning stretches from one idea all the way to its opposite. Drayton's "even bet" between Warwick and the Queen means that he has no idea who will win. In contrast, Twain's "you bet you" is a statement of certainty. In Twain's or Sayers' usage, it's as though uncertainty combines with moral conviction to produce a definite resolution. This is a dialectic in which doubt and determination form decisiveness.John Locke may have had something like this in mind when he wrote:"If we will disbelieve everything, because we cannot certainly know all things; we shall do muchwhat as wisely as he, who would not use his legs, but sit still and perish, because he had no wings to fly." (An Essay Concerning Human Understanding, 1706, I.i.5)The absurdity of Locke's wingless gentleman starving in his chair leads us to believe, and to act, despite our doubts. The moral imperative of survival sweeps aside the paralysis of uncertainty. The consequence of unabated doubt - paralysis - induces doubt's opposite: decisiveness.But rational creatures must have some method for reasoning around their uncertainties. Locke does not intend for us to simply ignore our ignorance. But if we have no way to place bets - if the odds simply are unknown - then what are we to do? We cannot "sit still and perish".This is where the strategy of robustness comes in.'Robust' means 'Strong and hardy; sturdy; healthy'. By implication, something that is robust is 'not easily damaged or broken, resilient'. A statistical test is robust if it yields 'approximately correct results despite the falsity of certain of the assumptions underlying it' or despite errors in the data. (OED)A decision is robust if its outcome is satisfactory despite error in the information and understanding which justified or motivated the decision. A robust decision is resilient to surprise, immune to ignorance.It is no coincidence that the colloquial use of the word 'bet' includes concepts of both chance and certainty. A good bet can tolerate large deviation from certainty, large error of information. A good bet is robust to surprise. 'You bet you' does not mean that the world is certain. It means that the outcome is certain to be acceptable, regardless of how the world turns out. The scout will handle the outfit even if there is a rogue in the ranks; Twain will get there on time despite snags and surprises. A good bet is robust to the unknown. You bet you!An extended and more formal discussion of these issues can be found elsewhere. Full Article betting robustness
b Beware the Rareness Illusion When Exploring the Unknown By decisions-and-info-gaps.blogspot.com Published On :: Thu, 06 Oct 2011 12:14:00 +0000 Here's a great vacation idea. Spend the summer roaming the world in search of the 10 lost tribes of Israel, exiled from Samaria by the Assyrians 2700 years ago (2 Kings 17:6). Or perhaps you'd like to search for Prester John, the virtuous ruler of a kingdom lost in the Orient? Or would you rather trace the gold-laden kingdom of Ophir (1 Kings 9:28)? Or do you prefer the excitement of tracking the Amazons, that nation of female warriors? Or perhaps the naval power mentioned by Plato, operating from the island of Atlantis? Or how about unicorns, or the fountain of eternal youth? The Unknown is so vast that the possibilities are endless.Maybe you don't believe in unicorns. But Plato evidently "knew" about the island of Atlantis. The conquest of Israel is known from Assyrian archeology and from the Bible. That you've never seen a Reubenite or a Naphtalite (or a unicorn) means that they don't exist?It is true that when something really does not exist, one might spend a long time futilely looking for it. Many people have spent enormous energy searching for lost tribes, lost gold, and lost kingdoms. Why is it so difficult to decide that what you're looking for really isn't there? The answer, ironically, is that the world has endless possibilities for discovery and surprise.Let's skip vacation plans and consider some real-life searches. How long should you (or the Libyans) look for Muammar Qaddafi? If he's not in the town of Surt, maybe he's Bani Walid, or Algeria, or Timbuktu? How do you decide he cannot be found? Maybe he was pulverized by a NATO bomb. It's urgent to find the suicide bomber in the crowded bus station before it's too late - if he's really there. You'd like to discover a cure for AIDS, or a method to halt the rising global temperature, or a golden investment opportunity in an emerging market, or a proof of the parallel postulate of Euclidean geometry.Let's focus our question. Suppose you are looking for something, and so far you have only "negative" evidence: it's not here, it's not there, it's not anywhere you've looked. Why is it so difficult to decide, conclusively and confidently, that it simply does not exist?This question is linked to a different question: how to make the decision that "it" (whatever it is) does not exist. We will focus on the "why" question, and leave the "how" question to students of decision theories such as statistics, fuzzy logic, possibility theory, Dempster-Shafer theory and info-gap theory. (If you're interested in an info-gap application to statistics, here is an example.)Answers to the "why" question can be found in several domains.Psychology provides some answers. People can be very goal oriented, stubborn, and persistent. Marco Polo didn't get to China on a 10-hour plane flight. The round trip took him 24 years, and he didn't travel business class.Ideology is a very strong motivator. When people believe something strongly, it is easy for them to ignore evidence to the contrary. Furthermore, for some people, the search itself is valued more than the putative goal.The answer to the "why" question that I will focus on is found by contemplating The Endless Unknown. It is so vast, so unstructured, so, well ..., unknown, that we cannot calibrate our negative evidence to decide that whatever we're looking for just ain't there.I'll tell a true story.I was born in the US and my wife was born in Israel, but our life-paths crossed, so to speak, before we were born. She had a friend whose father was from Europe and lived for a while - before the friend was born - with a cousin of his in my home town. This cousin was - years later - my 3rd grade teacher. My school teacher was my future wife's friend's father's cousin.Amazing coincidence. This convoluted sequence of events is certainly rare. How many of you can tell the very same story? But wait a minute. This convoluted string of events could have evolved in many many different ways, each of which would have been an equally amazing coincidence. The number of similar possible paths is namelessly enormous, uncountably humongous. In other words, potential "rare" events are very numerous. Now that sounds like a contradiction (we're getting close to some of Zeno's paradoxes, and Aristotle thought Zeno was crazy). It is not a contradiction; it is only a "rareness illusion" (something like an optical illusion). The specific event sequence in my story is unique, which is the ultimate rarity. We view this sequence as an amazing coincidence because we cannot assess the number of similar sequences. Surprising strings of events occur not infrequently because the number of possible surprising strings is so unimaginably vast. The rareness illusion is the impression of rareness arising from our necessary ignorance of the vast unknown. "Necessary" because, by definition, we cannot know what is unknown. "Vast" because the world is so rich in possibilities.The rareness illusion is a false impression, a mistake. For instance, it leads people to wrongly goggle at strings of events - rare in themselves - even though "rare events" are numerous and "amazing coincidences" occur all the time. An appreciation of the richness and boundlessness of the Unknown is an antidote for the rareness illusion.Recognition of the rareness illusion is the key to understanding why it is so difficult to confidently decide, based on negative evidence, that what you're looking for simply does not exist.One might be inclined to reason as follows. If you're looking for something, then look very thoroughly, and if you don't find it, then it's not there. That is usually sound and sensible advice, and often "looking thoroughly" will lead to discovery.However, the number of ways that we could overlook something that really is there is enormous. It is thus very difficult to confidently conclude that the search was thorough and that the object cannot be found. Take the case of your missing house keys. They dropped from your pocket in the car, or on the sidewalk and somebody picked them up, or you left them in the lock when you left the house, or or or .... Familiarity with the rareness illusion makes it very difficult to decide that you have searched thoroughly. If you think that the only contingencies not yet explored are too exotic to be relevant (a raven snatched them while you were daydreaming about that enchanting new employee), then think again, because you've been blinded by a rareness illusion. The number of such possibilities is so vastly unfathomable that you cannot confidently say that all of them are collectively negligible. Recognition of the rareness illusion prevents you from confidently concluding that what you are seeking simply does not exist.Many quantitative tools grapple with the rareness illusion. We mentioned some decision theories earlier. But because the rareness illusion derives from our necessary ignorance of the vast unknown, one must always beware.Looking for an exciting vacation? The Endless Unknown is the place to go. Full Article rareness illusion
b Squirrels and Stock Brokers, Or: Innovation Dilemmas, Robustness and Probability By decisions-and-info-gaps.blogspot.com Published On :: Sun, 09 Oct 2011 11:51:00 +0000 Decisions are made in order to achieve desirable outcomes. An innovation dilemma arises when a seemingly more attractive option is also more uncertain than other options. In this essay we explore the relation between the innovation dilemma and the robustness of a decision, and the relation between robustness and probability. A decision is robust to uncertainty if it achieves required outcomes despite adverse surprises. A robust decision may differ from the seemingly best option. Furthermore, robust decisions are not based on knowledge of probabilities, but can still be the most likely to succeed.Squirrels, Stock-Brokers and Their DilemmasDecision problems.Imagine a squirrel nibbling acorns under an oak tree. They're pretty good acorns, though a bit dry. The good ones have already been taken. Over in the distance is a large stand of fine oaks. The acorns there are probably better. But then, other squirrels can also see those trees, and predators can too. The squirrel doesn't need to get fat, but a critical caloric intake is necessary before moving on to other activities. How long should the squirrel forage at this patch before moving to the more promising patch, if at all?Imagine a hedge fund manager investing in South African diamonds, Australian Uranium, Norwegian Kroners and Singapore semi-conductors. The returns have been steady and good, but not very exciting. A new hi-tech start-up venture has just turned up. It looks promising, has solid backing, and could be very interesting. The manager doesn't need to earn boundless returns, but it is necessary to earn at least a tad more than the competition (who are also prowling around). How long should the manager hold the current portfolio before changing at least some of its components?These are decision problems, and like many other examples, they share three traits: critical needs must be met; the current situation may or may not be adequate; other alternatives look much better but are much more uncertain. To change, or not to change? What strategy to use in making a decision? What choice is the best bet? Betting is a surprising concept, as we have seen before; can we bet without knowing probabilities?Solution strategies.The decision is easy in either of two extreme situations, and their analysis will reveal general conclusions.One extreme is that the status quo is clearly insufficient. For the squirrel this means that these crinkled rotten acorns won't fill anybody's belly even if one nibbled here all day long. Survival requires trying the other patch regardless of the fact that there may be many other squirrels already there and predators just waiting to swoop down. Similarly, for the hedge fund manager, if other funds are making fantastic profits, then something has to change or the competition will attract all the business.The other extreme is that the status quo is just fine, thank you. For the squirrel, just a little more nibbling and these acorns will get us through the night, so why run over to unfamiliar oak trees? For the hedge fund manager, profits are better than those of any credible competitor, so uncertain change is not called for.From these two extremes we draw an important general conclusion: the right answer depends on what you need. To change, or not to change, depends on what is critical for survival. There is no universal answer, like, "Always try to improve" or "If it's working, don't fix it". This is a very general property of decisions under uncertainty, and we will call it preference reversal. The agent's preference between alternatives depends on what the agent needs in order to "survive".The decision strategy that we have described is attuned to the needs of the agent. The strategy attempts to satisfy the agent's critical requirements. If the status quo would reliably do that, then stay put; if not, then move. Following the work of Nobel Laureate Herbert Simon, we will call this a satisficing decision strategy: one which satisfies a critical requirement."Prediction is always difficult, especially of the future." - Robert Storm PetersenNow let's consider a different decision strategy that squirrels and hedge fund managers might be tempted to use. The agent has obtained information about the two alternatives by signals from the environment. (The squirrel sees grand verdant oaks in the distance, the fund manager hears of a new start up.) Given this information, a prediction can be made (though the squirrel may make this prediction based on instincts and without being aware of making it). Given the best available information, the agent predicts which alternative would yield the better outcome. Using this prediction, the decision strategy is to choose the alternative whose predicted outcome is best. We will call this decision strategy best-model optimization. Note that this decision strategy yields a single universal answer to the question facing the agent. This strategy uses the best information to find the choice that - if that information is correct - will yield the best outcome. Best-model optimization (usually) gives a single "best" decision, unlike the satisficing strategy that returns different answers depending on the agent's needs.There is an attractive logic - and even perhaps a moral imperative - to use the best information to make the best choice. One should always try to do one's best. But the catch in the argument for best-model optimization is that the best information may actually be grievously wrong. Those fine oak trees might be swarming with insects who've devoured the acorns. Best-model optimization ignores the agent's central dilemma: stay with the relatively well known but modest alternative, or go for the more promising but more uncertain alternative."Tsk, tsk, tsk" says our hedge fund manager. "My information already accounts for the uncertainty. I have used a probabilistic asset pricing model to predict the likelihood that my profits will beat the competition for each of the two alternatives."Probabilistic asset pricing models are good to have. And the squirrel similarly has evolved instincts that reflect likelihoods. But a best-probabilistic-model optimization is simply one type of best-model optimization, and is subject to the same vulnerability to error. The world is full of surprises. The probability functions that are used are quite likely wrong, especially in predicting the rare events that the manager is most concerned to avoid.Robustness and ProbabilityNow we come to the truly amazing part of the story. The satisficing strategy does not use any probabilistic information. Nonetheless, in many situations, the satisficing strategy is actually a better bet (or at least not a worse bet), probabilistically speaking, than any other strategy, including best-probabilistic-model optimization. We have no probabilistic information in these situations, but we can still maximize the probability of success (though we won't know the value of this maximum).When the satisficing decision strategy is the best bet, this is, in part, because it is more robust to uncertainty than another other strategy. A decision is robust to uncertainty if it achieves required outcomes even if adverse surprises occur. In many important situations (though not invariably), more robustness to uncertainty is equivalent to being more likely to succeed or survive. When this is true we say that robustness is a proxy for probability.A thorough analysis of the proxy property is rather technical. However, we can understand the gist of the idea by considering a simple special case.Let's continue with the squirrel and hedge fund examples. Suppose we are completely confident about the future value (in calories or dollars) of not making any change (staying put). In contrast, the future value of moving is apparently better though uncertain. If staying put would satisfy our critical requirement, then we are absolutely certain of survival if we do not change. Staying put is completely robust to surprises so the probability of success equals 1 if we stay put, regardless of what happens with the other option. Likewise, if staying put would not satisfy our critical requirement, then we are absolutely certain of failure if we do not change; the probability of success equals 0 if we stay, and moving cannot be worse. Regardless of what probability distribution describes future outcomes if we move, we can always choose the option whose likelihood of success is greater (or at least not worse). This is because staying put is either sure to succeed or sure to fail, and we know which.This argument can be extended to the more realistic case where the outcome of staying put is uncertain and the outcome of moving, while seemingly better than staying, is much more uncertain. The agent can know which option is more robust to uncertainty, without having to know probability distributions. This implies, in many situations, that the agent can choose the option that is a better bet for survival.Wrapping UpThe skillful decision maker not only knows a lot, but is also able to deal with conflicting information. We have discussed the innovation dilemma: When choosing between two alternatives, the seemingly better one is also more uncertain.Animals, people, organizations and societies have developed mechanisms for dealing with the innovation dilemma. The response hinges on tuning the decision to the agent's needs, and robustifying the choice against uncertainty. This choice may or may not coincide with the putative best choice. But what seems best depends on the available - though uncertain - information.The commendable tendency to do one's best - and to demand the same of others - can lead to putatively optimal decisions that may be more vulnerable to surprise than other decisions that would have been satisfactory. In contrast, the strategy of robustly satisfying critical needs can be a better bet for survival. Consider the design of critical infrastructure: flood protection, nuclear power, communication networks, and so on. The design of such systems is based on vast knowledge and understanding, but also confronts bewildering uncertainties and endless surprises. We must continue to improve our knowledge and understanding, while also improving our ability to manage the uncertainties resulting from the expanding horizon of our efforts. We must identify the critical goals and seek responses that are immune to surprise. Full Article betting innovation dilemma probability proxy property robustness
b The Language of Science and the Tower of Babel By decisions-and-info-gaps.blogspot.com Published On :: Mon, 31 Oct 2011 06:23:00 +0000 And God said: Behold one people with one language for them all ... and now nothing that they venture will be kept from them. ... [And] there God mixed up the language of all the land. (Genesis, 11:6-9)"Philosophy is written in this grand book the universe, which stands continually open to our gaze. But the book cannot be understood unless one first learns to comprehend the language and to read the alphabet in which it is composed. It is written in the language of mathematics." Galileo GalileiLanguage is power over the unknown. Mathematics is the language of science, and computation is the modern voice in which this language is spoken. Scientists and engineers explore the book of nature with computer simulations of swirling galaxies and colliding atoms, crashing cars and wind-swept buildings. The wonders of nature and the powers of technological innovation are displayed on computer screens, "continually open to our gaze." The language of science empowers us to dispel confusion and uncertainty, but only with great effort do we change the babble of sounds and symbols into useful, meaningful and reliable communication. How we do that depends on the type of uncertainty against which the language struggles.Mathematical equations encode our understanding of nature, and Galileo exhorts us to learn this code. One challenge here is that a single equation represents an infinity of situations. For instance, the equation describing a flowing liquid captures water gushing from a pipe, blood coursing in our veins, and a droplet splashing from a puddle. Gazing at the equation is not at all like gazing at the droplet. Understanding grows by exposure to pictures and examples. Computations provide numerical examples of equations that can be realized as pictures. Computations can simulate nature, allowing us to explore at our leisure.Two questions face the user of computations: Are we calculating the correct equations? Are we calculating the equations correctly? The first question expresses the scientist's ignorance - or at least uncertainty - about how the world works. The second question reflects the programmer's ignorance or uncertainty about the faithfulness of the computer program to the equations. Both questions deal with the fidelity between two entities. However, the entities involved are very different and the uncertainties are very different as well.The scientist's uncertainty is reduced by the ingenuity of the experimenter. Equations make predictions that can be tested by experiment. For instance, Galileo predicted that small and large balls will fall at the same rate, as he is reported to have tested from the tower of Pisa. Equations are rejected or modified when their predictions don't match the experimenter's observation. The scientist's uncertainty and ignorance are whittled away by testing equations against observation of the real world. Experiments may be extraordinarily subtle or difficult or costly because nature's unknown is so endlessly rich in possibilities. Nonetheless, observation of nature remorselessly cuts false equations from the body of scientific doctrine. God speaks through nature, as it were, and "the Eternal of Israel does not deceive or console." (1 Samuel, 15:29). When this observational cutting and chopping is (temporarily) halted, the remaining equations are said to be "validated" (but they remain on the chopping block for further testing).The programmer's life is, in one sense, more difficult than the experimenter's. Imagine a huge computer program containing millions of lines of code, the accumulated fruit of thousands of hours of effort by many people. How do we verify that this computation faithfully reflects the equations that have ostensibly been programmed? Of course they've been checked again and again for typos or logical faults or syntactic errors. Very clever methods are available for code verification. Nonetheless, programmers are only human, and some infidelity may slip through. What remorseless knife does the programmer have with which to verify that the equations are correctly calculated? Testing computation against observation does not allow us to distinguish between errors in the equations, errors in the program, and compensatory errors in both.The experimenter compares an equation's prediction against an observation of nature. Like the experimenter, the programmer compares the computation against something. However, for the programmer, the sharp knife of nature is not available. In special cases the programmer can compare against a known answer. More frequently the programmer must compare against other computations which have already been verified (by some earlier comparison). The verification of a computation - as distinct from the validation of an equation - can only use other high-level human-made results. The programmer's comparisons can only be traced back to other comparisons. It is true that the experimenter's tests are intermediated by human artifacts like calipers or cyclotrons. Nonetheless, bedrock for the experimenter is the "reality out there". The experimenter's tests can be traced back to observations of elementary real events. The programmer does not have that recourse. One might say that God speaks to the experimenter through nature, but the programmer has no such Voice upon which to rely.The tower built of old would have reached the heavens because of the power of language. That tower was never completed because God turned talk into babble and dispersed the people across the land. Scholars have argued whether the story prescribes a moral norm, or simply describes the way things are, but the power of language has never been disputed.The tower was never completed, just as science, it seems, has a long way to go. Genius, said Edison, is 1 percent inspiration and 99 percent perspiration. A good part of the sweat comes from getting the language right, whether mathematical equations or computer programs.Part of the challenge is finding order in nature's bubbling variety. Each equation captures a glimpse of that order, adding one block to the structure of science. Furthermore, equations must be validated, which is only a stop-gap. All blocks crumble eventually, and all equations are fallible and likely to be falsified.Another challenge in science and engineering is grasping the myriad implications that are distilled into an equation. An equation compresses and summarizes, while computer simulations go the other way, restoring detail and specificity. The fidelity of a simulation to the equation is usually verified by comparing against other simulations. This is like the dictionary paradox: using words to define words.It is by inventing and exploiting symbols that humans have constructed an orderly world out of the confusing tumult of experience. With symbols, like with blocks in the tower, the sky is the limit. Full Article
