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chocolate mousse with honeycomb and espresso sauce

honeycomb 40 g (11/2 oz) honey 70 g (21/2 oz) glucose syrup 200 g (7 oz) caster (superfine) sugar 1 teaspoon bicarbonate of soda (baking soda), sifted mousse 200 g (7 oz/11/3 cups) chopped good-quality dark chocolate, such as couverture (see note, page 235) 40 g (11/2 oz) unsalted butter, chopped 4 eggs, separated 150 g (51/2 oz/2/3 heaped cup) sugar espresso sauce 250 ml (9 fl oz/1 cup) espresso coffee 100 g (31/2 oz/1/2 cup) sugar 2 tablespoons kahlua




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Association of BMI, Fitness, and Mortality in Patients With Diabetes: Evaluating the Obesity Paradox in the Henry Ford Exercise Testing Project (FIT Project) Cohort

OBJECTIVE

To determine the effect of fitness on the association between BMI and mortality among patients with diabetes.

RESEARCH DESIGN AND METHODS

We identified 8,528 patients with diabetes (self-report, medication use, or electronic medical record diagnosis) from the Henry Ford Exercise Testing Project (FIT Project). Patients with a BMI <18.5 kg/m2 or cancer were excluded. Fitness was measured as the METs achieved during a physician-referred treadmill stress test and categorized as low (<6), moderate (6–9.9), or high (≥10). Adjusted hazard ratios for mortality were calculated using standard BMI (kilograms per meter squared) cutoffs of normal (18.5–24.9), overweight (25–29.9), and obese (≥30). Adjusted splines centered at 22.5 kg/m2 were used to examine BMI as a continuous variable.

RESULTS

Patients had a mean age of 58 ± 11 years (49% women) with 1,319 deaths over a mean follow-up of 10.0 ± 4.1 years. Overall, obese patients had a 30% lower mortality hazard (P < 0.001) compared with normal-weight patients. In adjusted spline modeling, higher BMI as a continuous variable was predominantly associated with a lower mortality risk in the lowest fitness group and among patients with moderate fitness and BMI ≥30 kg/m2. Compared with the lowest fitness group, patients with higher fitness had an ~50% (6–9.9 METs) and 70% (≥10 METs) lower mortality hazard regardless of BMI (P < 0.001).

CONCLUSIONS

Among patients with diabetes, the obesity paradox was less pronounced for patients with the highest fitness level, and these patients also had the lowest risk of mortality.




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Association Between the Use of Antidepressants and the Risk of Type 2 Diabetes: A Large, Population-Based Cohort Study in Japan

OBJECTIVE

This study aimed to reveal the associations between the risk of new-onset type 2 diabetes and the duration of antidepressant use and the antidepressant dose, and between antidepressant use after diabetes onset and clinical outcomes.

RESEARCH DESIGN AND METHODS

In this large-scale retrospective cohort study in Japan, new users of antidepressants (exposure group) and nonusers (nonexposure group), aged 20–79 years, were included between 1 April 2006 and 31 May 2015. Patients with a history of diabetes or receipt of antidiabetes treatment were excluded. Covariates were adjusted by using propensity score matching; the associations were analyzed between risk of new-onset type 2 diabetes and the duration of antidepressant use/dose of antidepressant in the exposure and nonexposure groups by using Cox proportional hazards models. Changes in glycated hemoglobin (HbA1c) level were examined in groups with continuous use, discontinuation, or a reduction in the dose of antidepressants.

RESULTS

Of 90,530 subjects, 45,265 were in both the exposure and the nonexposure group after propensity score matching; 5,225 patients (5.8%) developed diabetes. Antidepressant use was associated with the risk of diabetes onset in a time- and dose-dependent manner. The adjusted hazard ratio was 1.27 (95% CI 1.16–1.39) for short-term low-dose and 3.95 (95% CI 3.31–4.72) for long-term high-dose antidepressant use. HbA1c levels were lower in patients who discontinued or reduced the dose of antidepressants (F[2,49] = 8.17; P < 0.001).

CONCLUSIONS

Long-term antidepressant use increased the risk of type 2 diabetes onset in a time- and dose-dependent manner. Glucose tolerance improved when antidepressants were discontinued or the dose was reduced after diabetes onset.




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Turning the Tide: Addressing the Long-Term Challenges of EU Mobility for Sending Countries

Amid ongoing debates about the costs and benefits of free movement, this MPI webinar examines evidence from the EU-funded REMINDER project on different types of East-West mobility. Speakers examine big-picture trends of East-West migration; consider possible policy responses at regional, national, and EU levels to alleviate some of the challenges; and reflect on realistic actions that could be taken under a new European Commission.




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An Uneven Landscape: The Differing State Approaches to English Learner Policies under ESSA

Experts share how states have approached Every Student Succeeds Act (ESSA) implementation, areas where the law and state efforts to support English Learners can be improved, and findings from the compendium, The Patchy Landscape of State English Learner Policies under ESSA




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Seasonal Worker Programs in Europe: Lessons Learned and Ways Forward

As the European Union prepares to review the implementation of its Seasonal Workers Directive, as well as countries such as the United Kingdom continue to explore new approaches to selecting seasonal workers, this webinar features findings from a policy brief on the topic. 




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Expert Podcast: Understanding How English Learners Count in ESSA Reporting

This podcast features a discussion between MPI's Margie McHugh and Julie Sugarman about how to understand the varying composition of states' English Learner (EL) subgroup under ESSA, and why understanding these technical differences matters when making decisions about how ELs and schools are faring. They also talk about different groups of ELs: newcomers, students with interrupted formal education, and long-term ELs, and data collection around these different cohorts.




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Immigration and the U.S.-Mexico Border during the Pandemic: A Conversation with Members of Congress

In this bipartisan discussion, two border-state members of Congress—Rep. Veronica Escobar and Rep. Dan Crenshaw—discuss the response to the coronavirus outbreak, how it is affecting the interconnected border region, and what the future might hold.
 




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In the Age of Trump: Populist Backlash and Progressive Resistance Create Divergent State Immigrant Integration Contexts

As long-simmering passions related to federal immigration policies have come to a full boil, less noted but no less important debates are taking place at state and local levels with regards to policies affecting immigrants and their children. As states are increasingly diverging in their responses, this report examines how some of the key policies and programs that support long-term integration success are faring in this volatile era.




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Addressing Trauma in Young Children in Immigrant and Refugee Families through Early Childhood Programs

During this webinar, speakers provide an overview of an MPI policy brief that seeks to raise awareness of the intersection of trauma and early childhood development, and how U.S. early childhood programs could more effectively address this trauma in young children in refugee and immigrant households. The participants discuss efforts to integrate trauma-informed approaches into early childhood systems and how home visiting services can effectively address trauma and mental health through a two-generation approach.




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As U.S. Health-Care System Buckles under Pandemic, Immigrant &amp; Refugee Professionals Could Represent a Critical Resource

In a time of critical shortages of U.S. health-care workers during the COVID-19 pandemic, retired doctors are being called back to work and medical students are graduating on a fast track. There is another important pool that could be tapped: Immigrants and refugees who have college degrees in health fields but are working in low-skilled jobs or out of work. MPI estimates 263,000 immigrants are experiencing skill underutilization and could be a valuable resource.




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The Digital Transformation Playbook: Rethink Your Business for the Digital Age

Every business begun before the Internet now faces the same challenge: How to transform to compete in a digital economy? This is the leadership challenge examined by BRITE founder and Columbia Business School faculty member David Rogers in his newest book, The Digital Transformation Playbook (April 5, 2016; Columbia Business School Publishing). In the book, […]




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Reflections on Business, Leadership, and Branding: Shelly Lazarus ’70

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 […]




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Diabetes and Back Pain: Markers of Diabetes Disease Progression Are Associated With Chronic Back Pain

Lorenzo Rinaldo
Jul 1, 2017; 35:126-131
Feature Articles




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Diapression: An Integrated Model for Understanding the Experience of Individuals With Co-Occurring Diabetes and Depression

Paul Ciechanowski
Apr 1, 2011; 29:43-49
Feature Articles




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What's So Tough About Taking Insulin? Addressing the Problem of Psychological Insulin Resistance in Type 2 Diabetes

William H. Polonsky
Jul 1, 2004; 22:147-150
Practical Pointers




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Heroic Consciousness: What it is and How to Acquire it

By Scott T. Allison This blog post is excerpted from: Allison, S. T. (2019). Heroic consciousness. Heroism Science, 4, 1-43.   The philosopher Yuval Noah Harari (2018) recently described consciousness as “the greatest mystery in the universe”. What exactly is heroic consciousness? It is a way of seeing the world, perceiving reality, and making decisions … Continue reading Heroic Consciousness: What it is and How to Acquire it




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No-Failure Design and Disaster Recovery: Lessons from Fukushima

One of the striking aspects of the early stages of the nuclear accident at Fukushima-Daiichi last March was the nearly total absence of disaster recovery capability. For instance, while Japan is a super-power of robotic technology, the nuclear authorities had to import robots from France for probing the damaged nuclear plants. Fukushima can teach us an important lesson about technology.

The failure of critical technologies can be disastrous. The crash of a civilian airliner can cause hundreds of deaths. The meltdown of a nuclear reactor can release highly toxic isotopes. Failure of flood protection systems can result in vast death and damage. Society therefore insists that critical technologies be designed, operated and maintained to extremely high levels of reliability. We benefit from technology, but we also insist that the designers and operators "do their best" to protect us from their dangers.

Industries and government agencies who provide critical technologies almost invariably act in good faith for a range of reasons. Morality dictates responsible behavior, liability legislation establishes sanctions for irresponsible behavior, and economic or political self-interest makes continuous safe operation desirable.

The language of performance-optimization  not only doing our best, but also achieving the best  may tend to undermine the successful management of technological danger. A probability of severe failure of one in a million per device per year is exceedingly  and very reassuringly  small. When we honestly believe that we have designed and implemented a technology to have vanishingly small probability of catastrophe, we can honestly ignore the need for disaster recovery.

Or can we?

Let's contrast this with an ethos that is consistent with a thorough awareness of the potential for adverse surprise. We now acknowledge that our predictions are uncertain, perhaps highly uncertain on some specific points. We attempt to achieve very demanding outcomes  for instance vanishingly small probabilities of catastrophe  but we recognize that our ability to reliably calculate such small probabilities is compromised by the deficiency of our knowledge and understanding. We robustify ourselves against those deficiencies by choosing a design which would be acceptable over a wide range of deviations from our current best understanding. (This is called "robust-satisficing".) Not only does "vanishingly small probability of failure" still entail the possibility of failure, but our predictions of that probability may err.

Acknowledging the need for disaster recovery capability (DRC) is awkward and uncomfortable for designers and advocates of a technology. We would much rather believe that DRC is not needed, that we have in fact made catastrophe negligible. But let's not conflate good-faith attempts to deal with complex uncertainties, with guaranteed outcomes based on full knowledge. Our best models are in part wrong, so we robustify against the designer's bounded rationality. But robustness cannot guarantee success. The design and implementation of DRC is a necessary part of the design of any critical technology, and is consistent with the strategy of robust satisficing.

One final point: moral hazard and its dilemma. The design of any critical technology entails two distinct and essential elements: failure prevention and disaster recovery. What economists call a `moral hazard' exists since the failure prevention team might rely on the disaster-recovery team, and vice versa. Each team might, at least implicitly, depend on the capabilities of the other team, and thereby relinquish some of its own responsibility. Institutional provisions are needed to manage this conflict.

The alleviation of this moral hazard entails a dilemma. Considerations of failure prevention and disaster recovery must be combined in the design process. The design teams must be aware of each other, and even collaborate, because a single coherent system must emerge. But we don't want either team to relinquish any responsibility. On the one hand we want the failure prevention team to work as though there is no disaster recovery, and the disaster recovery team should presume that failures will occur. On the other hand, we want these teams to collaborate on the design.

This moral hazard and its dilemma do not obviate the need for both elements of the design. Fukushima has taught us an important lesson by highlighting the special challenge of high-risk critical technologies: design so failure cannot occur, and prepare to respond to the unanticipated.




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Robustness and Locke's Wingless Gentleman

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.




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The Pains of Progress

To measure time by how little we change is to find how little we've lived, 
but to measure time by how much we've lost is to wish we hadn't changed at all. Andre Aciman

The last frontier is not the Antarctic, or the oceans, or outer space. The last frontier is The Unknown. We mentioned in an earlier essay that uncertainty - which makes baseball and life interesting - is inevitable in the human world. Life will continue to be interesting as long as the world is rich in unknowns, waiting to be discovered. Progress is possible if propitious discoveries can be made. Progress, however, comes with costs.

The emblem of my university entwines a billowing smokestack and a cogwheel in the first letter of the institution's name. When this emblem was adopted (probably in 1951) these were optimistic symbols of progress. Cogwheels are no longer 'hi-tech' (though we still need them), and smoke has been banished from polite company. But our emblem is characteristic of industrial society which has seared Progress on our hearts and minds.

Progress is accompanied by painful tensions. On the one hand, progress is nurtured by stability, cooperation, and leisure. On the other hand, progress grows out of change, conflict, and stress. A society's progressiveness reflects its balance of each of these three pairs of attributes. In the most general terms, progressiveness reflects social and individual attitudes to uncertainty.

Let's consider the three pairs of attributes one at a time.

Change and stability. Not all change is progress, but all progress is change. Change is necessary for progress, by definition, and progress can be very disruptive. The disruptiveness sometimes arises from unexpected consequences. J.B.S. Haldane wrote in 1923 that "the late war is only an example of the disruptive result that we may constantly expect from the progress of science." On the other hand, progressives employ and build on existing capabilities. The entrepreneur depends on stable property rights before risking venture capital. The existing legal system is used to remove social injustice. Watt's steam engine extended Newcomen's more primitive model. The new building going up on campus next to my office is very disruptive, but the construction project depends on the continuity of the university despite the drilling and dust. Even revolutionaries exploit and react against the status quo, which must exist for a revolutionary to be able to revolt. (One can't revolt if nothing is revolting.) Progress grows from a patch of opportunity in a broad bed of certainty, and spreads out in unanticipated directions.

Conflict and cooperation. Conflict between vested interests and innovators is common. Watt protected his inventions with extensive patents which may have actually retarded the further development and commercialization of steam power. Conflict is also a mechanism for selecting successful ideas. Darwinian evolution and its social analogies proceed by more successful adaptations replacing less successful ones. On the other hand, cooperation enables specialization and expertise which are needed for innovation. The tool-maker cooperates with the farmer so better tools can be made more quickly, enhancing the farmer's productivity and the artisan's welfare. Conflicts arise over what constitutes progress. Stem cell research, genetic engineering, nuclear power technology: progress or plague? Cooperative collective decision making enables the constructive resolution of these value-based conflicts.

Stress and leisure. Challenge, necessity and stress all motivate innovation. If you have no problems, you are unlikely to be looking for solutions. On the other hand, the leisure to think and tinker is a great source of innovation. Subsistence societies have no resources for invention. In assessing the implications of industrial efficiency, Bertrand Russell praised idleness in 1932, writing: "In a world where no one is compelled to work more than four hours a day, every person possessed of scientific curiosity will be able to indulge it, and every painter will be able to paint without starving ...." Stress is magnified by the unknown consequences of the stressor, while leisure is possible only in the absence of fear.

New replaces Old. Yin and yang are complementary opposites that dynamically interact. In Hegel's dialectic, tension between contradictions is resolved by synthesis. Human history is written by the victors, who sometimes hardly mention those swept into Trotsky's "dustbin of history". "In the evening resides weeping; in the morning: joy." (Psalm 30:6). Change and stability; conflict and cooperation; stress and leisure.

No progress without innovation; no innovation without discovery; no discovery without the unknown; no unknown without fear. There is no progress without pain.



  • change and stability
  • conflict and cooperation
  • costs of progress
  • progress
  • stress and leisure

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Beware the Rareness Illusion When Exploring the Unknown

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. 




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Squirrels and Stock Brokers, Or: Innovation Dilemmas, Robustness and Probability

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 Dilemmas




Decision 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 Petersen

Now 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 Probability

Now 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 Up

The 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. 




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Mind or Stomach? Imagination or Necessity?

"An army marches on its stomach" said Napoleon, who is also credited with saying "Imagination rules the world". Is history driven by raw necessity and elementary needs? Or is history hewn by people from their imagination, dreams and ideas?

The answer is simple: 'Both'. The challenge is to untangle imagination from necessity. Consider these examples:

An ancient Jewish saying is "Without flour, there is no Torah. Without Torah there is no flour." (Avot 3:17) Scholars don't eat much, but they do need to eat. And if you feed them, they produce wonders.

Give a typewriter to a monkey and he might eventually tap out Shakespeare's sonnets, but it's not very likely. Give that monkey an inventive mind and he will produce poetry, a vaccine against polio, and the atom bomb. Why the bomb? He needed it.

Necessity is the mother of invention, they say, but it's actually a two-way street. For instance, human inventiveness includes dreams of cosmic domination, leading to war. Hence the need for that bomb. Satisfying a need, like the need for flour, induces inventiveness. And this inventiveness, like the discovery of genetically modified organisms, creates new needs. Necessity induces inventiveness, and inventiveness creates new dangers, challenges and needs. This cycle is endless because the realm of imagination is boundless, far greater than prosaic reality, as we discussed elsewhere.

Imagination and necessity are intertwined, but still are quite different. Necessity focusses primarily on what we know, while imagination focusses on the unknown.

We know from experience that we need food, shelter, warmth, love, and so on. These requirements force themselves on our awareness. Even the need for protection against surprise is known, though the surprise is not.

Imagination operates in the realm of the unknown. We seek the new, the interesting, or the frightful. Imagination feeds our fears of the unknown and nurtures our hopes for the unimaginable. We explore the bounds of the possible and try breaking through to the impossible.

Mind or stomach? Imagination or necessity? Every 'known' has an 'unknown' lurking behind it, and every 'unknown' may some day be discovered or dreamed into existence. Every mind has a stomach, and a stomach with no mind is not human.






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Was this yard sale successful?

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Depression is one of the most common mental health issues in the United States. In fact, according to the National Institutes of Mental Health, 1 in 14 Americans experienced an episode of major depression in the last year alone. Given the circumstances surrounding the current COVID-19 coronavirus pandemic, many in the mental health community predict that the number will be even higher this year because these circumstances are creating a “perfect storm” of depression risks. The widespread prevalence of depression has a lot of implications for our lives, even if we don’t personally have depression ourselves. Many of us will have loved ones, such as a romantic partner, who develops depression at some point. In these situations, it’s common for people to wonder how they can help their partner most effectively. So what should they do?




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Pardons, commutations and bankruptcy laws are all tools of forgiveness within the US legal system. Are we using them frequently enough, and with fairness? Law professor Martha Minow outlines how these merciful measures can reinforce racial and economic inequality -- and makes the case for creating a system of restorative justice that focuses on accountability and reconciliation rather than punishment.




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Professorship in decision making at Wharton, Operations, Information and Decisions (OID), University of Pennsylvania

APPLICATION DEADLINE OCTOBER 15, 2019 The Operations, Information and Decisions Department at the Wharton School is seeking applicants for a full-time, tenure track, Assistant Professor faculty position in the area of decision-making. Our decision-making faculty is comprised of scholars with a diverse set of backgrounds and interests, and we encourage applicants with degrees in economics, […]

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