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Effects of MK-0941, a Novel Glucokinase Activator, on Glycemic Control in Insulin-Treated Patients With Type 2 Diabetes

By care.diabetesjournals.org
Published On :: 2011-11-21T22:32:39-08:00

OBJECTIVE

To assess the efficacy and safety of MK-0941, a glucokinase activator (GKA), when added to stable-dose insulin glargine in patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

In this double-blind study, 587 patients taking stable-dose insulin glargine (±metformin ≥1,500 mg/day) were randomized (1:1:1:1:1) to MK-0941 10, 20, 30, or 40 mg or matching placebo t.i.d. before meals (a.c.). This study included an initial 14-week, dose-ranging phase followed by a 40-week treatment phase during which patients were to be uptitrated as tolerated to 40 mg (or placebo) t.i.d. a.c. The primary efficacy end point was change from baseline in A1C at Week 14.

 RESULTS

At Week 14, A1C and 2-h postmeal glucose (PMG) improved significantly versus placebo with all MK-0941 doses. Maximal placebo-adjusted least squares mean changes from baseline in A1C (baseline A1C 9.0%) and 2-h PMG were –0.8% and –37 mg/dL (–2 mmol/L), respectively. No significant effects on fasting plasma glucose were observed at any dose versus placebo. By 30 weeks, the initial glycemic responses noted at 14 weeks were not sustained. MK-0941 at one or more doses was associated with significant increases in the incidence of hypoglycemia, triglycerides, systolic blood pressure, and proportion of patients meeting criteria for predefined limits of change for increased diastolic blood pressure.

 CONCLUSIONS

In patients receiving stable-dose insulin glargine, the GKA MK-0941 led to improvements in glycemic control that were not sustained. MK-0941 was associated with an increased incidence of hypoglycemia and elevations in triglycerides and blood pressure.


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Legal Migration Pathways to Europe for Low- and Middle-Skilled Migrants

By www.migrationpolicy.org
Published On :: Thu, 24 Oct 2019 11:17:54 -0400

This event hosted by MPI Europe and the Research Unit of the Expert Council of German Foundations on Integration and Migration featured a discussion on research into legal migration pathways for work and training for low- and middle-skilled migrants.


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Respuestas latinoamericanas a las crisis migratorias venezolanas y nicaragüenses

By www.migrationpolicy.org
Published On :: Mon, 04 Nov 2019 08:58:25 -0500

Responsables de políticos principales y partes interesadas de América Latina, así como representantes de instituciones internacionales claves, ofrecen sus puntos de vista sobre los desafíos futuros mientras gobiernos latinoamericanos buscan establecer las estrategias para responder a flujos migratorios forzados a gran escala, como los de Venezuela y Nicaragua.


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

By www.migrationpolicy.org
Published On :: Thu, 05 Dec 2019 15:26:24 -0500

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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Employment Services for Refugees: Leveraging Mainstream U.S. Systems and Funding

By www.migrationpolicy.org
Published On :: Mon, 27 Jan 2020 16:53:00 -0500

On this webinar, experts and state refugee resettlement program leaders discuss activities that can be key parts of a broader strategy for sustaining and improving employment services for refugees, including partnerships with experts in workforce development strategies, access to federal workforce development funding, and other policies and resources.


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Is the Door Closing? Latin American and Caribbean Responses to Venezuelan Migration

By www.migrationpolicy.org
Published On :: Thu, 13 Feb 2020 11:40:37 -0500

Fleeing crisis, nearly 4 million Venezuelans have moved to other Latin American and Caribbean countries over the past few years. This webinar marked the launch of a Latin American and Caribbean Migration Portal, and a report examining the migration and integration policy responses in the region. 


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Seasonal Worker Programs in Europe: Lessons Learned and Ways Forward

By www.migrationpolicy.org
Published On :: Fri, 28 Feb 2020 13:36:08 -0500

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

By www.migrationpolicy.org
Published On :: Wed, 11 Mar 2020 19:13:01 -0400

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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Migration & Coronavirus: A Complicated Nexus Between Migration Management and Public Health

By www.migrationpolicy.org
Published On :: Tue, 24 Mar 2020 15:13:37 -0400

This webinar, organized by MPI and the Zolberg Institute on Migration and Mobility at The New School, discussed migration policy responses around the globe in response to the COVID-19 pandemic, and examined where migration management and enforcement tools may be useful and where they may be ill-suited to advancing public health goals. 


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Expert Podcast: Meeting Seasonal Labor Needs in the Age of COVID-19

By www.migrationpolicy.org
Published On :: Tue, 31 Mar 2020 09:35:25 -0400

Governments are facing urgent pandemic-related questions. One of the more pressing ones: Who is going to harvest crops in countries that rely heavily on seasonal foreign workers? In this podcast, MPI experts examine ways in which countries could address labor shortages in agriculture, including recruiting native-born workers and letting already present seasonal workers stay longer. Catch an interesting discussion as border closures have halted the movement of seasonal workers even as crops are approaching harvest in some places.


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COVID-19 in Latin America: Tackling Health Care & Other Impacts for Vulnerable Migrant Populations

By www.migrationpolicy.org
Published On :: Mon, 06 Apr 2020 12:44:50 -0400

This MPI webinar brought together public health and migration experts to analyze the impact that COVID-19 preventative measures will have on vulnerable immigrants and refugees in Colombia and Latin America. Speakers also discussed how policymakers and international organizations can include migrant populations in their emergency response plans.


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

By www.migrationpolicy.org
Published On :: Wed, 22 Apr 2020 18:40:11 -0400

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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A Study of Pregnancy and Birth Outcomes among African-Born Women Living in Utah

By www.migrationpolicy.org
Published On :: Mon, 07 Nov 2016 14:30:31 -0500

Resettled African refugee women may experience particularly acute complications during pregnancy, birth, and the child's early infancy. Yet health care-providers and policymakers may not be aware of the particular challenges that these women and their children face. This report, examining women giving birth in Utah over a seven-year period, compares perinatal complications of the African born and a segment of the U.S. born.


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Through the Back Door: Remaking the Immigration System via the Expected “Public-Charge” Rule

By www.migrationpolicy.org
Published On :: Thu, 23 Aug 2018 11:11:59 -0400

A Trump administration “public-charge” rule expected to be unveiled soon could create the potential to significantly reshape family-based legal immigration to the United States—and reduce arrivals from Asia, Latin America, and Africa—by imposing a de facto financial test that 40 percent of the U.S. born themselves would fail, as this commentary explains.


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Health Insurance Test for Green-Card Applicants Could Sharply Cut Future U.S. Legal Immigration

By www.migrationpolicy.org
Published On :: Thu, 10 Oct 2019 11:55:01 -0400

A new Trump administration action requiring intending immigrants to prove they can purchase eligible health insurance within 30 days of arrival has the potential to block fully 65 percent of those who apply for a green card from abroad, MPI estimates.


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

By brandleadership.wordpress.com
Published On :: Wed, 17 Feb 2016 16:24:52 +0000

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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Thinking with AND: Insights from KIND’s story

By brandleadership.wordpress.com
Published On :: Fri, 24 Jun 2016 16:29:46 +0000

“I’m a confused Mexican Jew.” So says Daniel Lubetzky, Founder and CEO of KIND Snack, in his very personal interview with Columbia faculty member David Rogers at BRITE ’16. Their discussion touched on the many ideas behind KIND Snacks, from the beginnings of the company, to the strategic thinking that forces Lubetzky to stay away […]


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Algorhythms for Marketing Transformation

By brandleadership.wordpress.com
Published On :: Fri, 24 Jun 2016 17:24:16 +0000

We all understand that digital media, data, and analytics are driving transformations in society and business. Most marketers are now armed with case studies of what can be done differently, but many are still challenged with how to truly develop new ideas and execute new strategies to grow their business. Mitch Joel, President of Mirum […]


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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. Powers
Apr 1, 2016; 34:70-80
Position Statements


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The Death of the "1800-Calorie ADA Diet"

By clinical.diabetesjournals.org
Published On :: 2002-04-01

Irl B. Hirsch
Apr 1, 2002; 20:
Editorials


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Therapeutic Inertia is a Problem for All of Us

By clinical.diabetesjournals.org
Published On :: 2019-04-01

Stephen Brunton
Apr 1, 2019; 37:105-106
Editorials


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

By clinical.diabetesjournals.org
Published On :: 2011-04-01

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


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SGLT-2 Inhibitors: A New Mechanism for Glycemic Control

By clinical.diabetesjournals.org
Published On :: 2014-01-01

Edward C. Chao
Jan 1, 2014; 32:4-11
Feature Articles


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Self-Monitoring of Blood Glucose: The Basics

By clinical.diabetesjournals.org
Published On :: 2002-01-01

Evan M. Benjamin
Jan 1, 2002; 20:
Practical Pointers


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Persistence of Continuous Glucose Monitoring Use in a Community Setting 1 Year After Purchase

By clinical.diabetesjournals.org
Published On :: 2013-07-01

James Chamberlain
Jul 1, 2013; 31:106-109
Feature Articles


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Interdisciplinary Team Care for Diabetic Patients by Primary Care Physicians, Advanced Practice Nurses, and Clinical Pharmacists

By clinical.diabetesjournals.org
Published On :: 2011-04-01

David Willens
Apr 1, 2011; 29:60-68
Feature Articles


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Insulin Strategies for Primary Care Providers

By clinical.diabetesjournals.org
Published On :: 2002-01-01

Karen L. Herbst
Jan 1, 2002; 20:
Feature Articles


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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. Rotenstein
Oct 1, 2012; 30:138-150
Features


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Diabetes Management Issues for Patients With Chronic Kidney Disease

By clinical.diabetesjournals.org
Published On :: 2007-07-01

Kerri L. Cavanaugh
Jul 1, 2007; 25:90-97
Feature Articles


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Stigma in People With Type 1 or Type 2 Diabetes

By clinical.diabetesjournals.org
Published On :: 2017-01-01

Nancy F. Liu
Jan 1, 2017; 35:27-34
Feature Articles


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Building Therapeutic Relationships: Choosing Words That Put People First

By clinical.diabetesjournals.org
Published On :: 2017-01-01

Jane K. Dickinson
Jan 1, 2017; 35:51-54
Commentary


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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 Lorenzi
Sep 1, 2010; 28:157-162
Bridges to Excellence


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Engaging Patients in Education for Self-Management in an Accountable Care Environment

By clinical.diabetesjournals.org
Published On :: 2011-07-01

Christine A. Beebe
Jul 1, 2011; 29:123-126
Practical Pointers


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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 Anderson
Jan 1, 2008; 26:22-27
Bridges to Excellence


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Standards of Medical Care in Diabetes--2019 Abridged for Primary Care Providers

By clinical.diabetesjournals.org
Published On :: 2019-01-01

American Diabetes Association
Jan 1, 2019; 37:11-34
Position Statements


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The Disparate Impact of Diabetes on Racial/Ethnic Minority Populations

By clinical.diabetesjournals.org
Published On :: 2012-07-01

Edward A. Chow
Jul 1, 2012; 30:130-133
Diabetes Advocacy


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Standards of Medical Care in Diabetes--2016 Abridged for Primary Care Providers

By clinical.diabetesjournals.org
Published On :: 2016-01-01

American Diabetes Association
Jan 1, 2016; 34:3-21
Position Statements


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A Real-World Approach to Insulin Therapy in Primary Care Practice

By clinical.diabetesjournals.org
Published On :: 2005-04-01

Irl B. Hirsch
Apr 1, 2005; 23:78-86
Practical Pointers


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Standards of Medical Care in Diabetes--2018 Abridged for Primary Care Providers

By clinical.diabetesjournals.org
Published On :: 2018-01-01

American Diabetes Association
Jan 1, 2018; 36:14-37
Position Statements


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Standards of Medical Care in Diabetes--2017 Abridged for Primary Care Providers

By clinical.diabetesjournals.org
Published On :: 2017-01-01

American Diabetes Association
Jan 1, 2017; 35:5-26
Position Statements


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Standards of Medical Care in Diabetes--2015 Abridged for Primary Care Providers

By clinical.diabetesjournals.org
Published On :: 2015-04-01

American Diabetes Association
Apr 1, 2015; 33:97-111
Position Statements


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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 →


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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. 


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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 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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Can We Replay History?

By decisions-and-info-gaps.blogspot.com
Published On :: Thu, 10 Nov 2011 07:15:00 +0000


After the kids' party games and the birthday cake came the action-packed Steve McQueen movie. My friend's parents had rented a movie projector. They hooked up the reel and let it roll. But the high point came later when they ran the movie backwards. Bullets streamed back into guns, blows were retracted and fallen protagonists recoiled into action. The mechanism that pulls the celluloid film forward for normal showing, can pull the film in the reverse direction, rolling it back onto the feeder reel and showing the movie in reverse.

If you chuck a round pebble off a cliff it will fall in a graceful parabolic arch, gradually increasing its speed until it hits the ground. The same pebble, if shot from the point of impact, at the terminating angle and speed, will gracefully and obligingly retrace its path. (I'm ignoring wind and air friction that make things a bit more complicated.)

Deterministic mechanisms, like the movie reel mechanism or the law of gravity, are reversible.

History is different. Peoples' behavior is influenced by what they know. You pack an umbrella on a trip to the UK. Google develops search algorithms not search parties because their knowledge base is information technology not mountain trekking. Knowledge is powerful because it enables rational behavior: matching actions to goals. Knowledge transforms futile fumbling into intelligent behavior.

Knowledge underlies intelligent behavior, but knowledge is continually expanding. We discover new facts and relationships. We discover that things have changed. Therefore tomorrow's knowledge-based behavior will, to some extent, be unpredictable today because tomorrow's discoveries cannot be known today. Human behavior has an inherent element of indeterminism. Intelligent learning behavior cannot be completely predicted.

Personal and collective history does not unfold like a pre-woven rug. Human history is fundamentally different from the trajectory of a pebble tossed from a cliff. History is the process of uncovering the unknown and responding to this new knowledge. The existence of the unknown creates the possibility of free will. The discovery of new knowledge introduces indeterminism and irreversibility into history, as explained by the philosophers G.L.S. Shackle and Karl Popper.

Nonetheless history is not erratic because each increment of new knowledge adds to the store of what was learned before. Memory is not perfect, either of individuals or groups, but it is powerful. History happens in historical context. For instance, one cannot understand the recent revolutions and upheavals in the Arab world from the perspective of 18th century European revolutions; the historical backgrounds are too different, and the outcomes in the Middle East will be different as well. Innovation, even revolution, is spurred by new knowledge laid over the old. A female municipal official slapped a Tunisian street vendor, Mohamed Bouazizi. That slap crystalized Mr Bouazizi's knowledge of his helpless social impotence and lit the match with which he immolated himself and initiated conflagrations around the Mideast. New knowledge acts like thruster engines on the inertial body of memory. What is emerging in the Mideast is Middle Eastern, not European. What is emerging is the result of new knowledge: of the power of networking, of the mortality of dictators, of the limits of coercion, of the power of new knowledge itself and the possibilities embedded in tomorrow's unknowns.

Mistakes are made, even with the best intentions and the best possible knowledge. Even if analysts knew and understood all the actions of all actors on the stage of history, they still cannot know what those people will learn tomorrow and how that new knowledge will alter their behavior. Mistakes are made because history does not unwind like a celluloid reel.

That's not to say that analysts are never ignorant, negligent, stupid or malicious. It's to say that all actions are, in a sense, mistakes. Or, the biggest mistake of all is to think that we can know the full import of our actions. We cannot, because actions are tossed, like pebbles, into the dark pit of unknown possible futures. One cannot know all possible echoes, or whether some echo might be glass-shatteringly cataclysmic.

Mistakes can sometimes be corrected, but never undone. History cannot be run backwards, and you never get a second chance. Conversely, every instant is a new opportunity because the future is always uncertain. Uncertainty is the freedom to err, and the opportunity to create and discover. 


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Picking a Theory is Like Building a Boat at Sea

By decisions-and-info-gaps.blogspot.com
Published On :: Sun, 11 Dec 2011 12:14:00 +0000


"We are like sailors who on the open sea must reconstruct their ship
 but are never able to start afresh from the bottom." 
Otto Neurath's analogy in the words of Willard V. Quine

Engineers, economists, social planners, security strategists, and others base their plans and decisions on theories. They often argue long and hard over which theory to use. Is it ever right to use a theory that we know is empirically wrong, especially if a true (or truer) theory is available? Why is it so difficult to pick a theory?

Let's consider two introductory examples.

You are an engineer designing a robot. You must calculate the forces needed to achieve specified motions of the robotic arms. You can base these calculations on either of two theories. One theory assumes that an object comes to rest unless a force acts upon it. Let's call this axiom A. The other theory assumes that an object moves at constant speed unless a force acts upon it. Let's call this axiom G. Axiom A agrees with observation: Nothing moves continuously without the exertion of force; an object will come to rest unless you keep pushing it. Axiom G contradicts all observation; no experiment illustrates the perpetual motion postulated by the axiom. If all else is the same, which theory should you choose?

Axiom A is Aristotle's law of inertia, which contributed little to the development of mechanical dynamics. Axiom G is Galileo's law of inertia: one of the most fruitful scientific ideas of all time. Why is an undemonstrable assertion - axiom G - a good starting point for a theory?

Consider another example.

You are an economist designing a market-based policy to induce firms to reduce pollution. You will use an economic theory to choose between policies. One theory assumes that firms face pure competition, meaning that no single firm can influence market prices. Another theory provides agent-based game-theoretic characterization of how firms interact (without colluding) by observing and responding to price behavior of other firms and of consumers.

Pure competition is a stylized idealization (like axiom G). Game theory is much more realistic (like axiom A), but may obscure essential patterns in its massive detail. Which theory should you use?

We will not address the question of how to choose a theory upon which to base a decision. We will focus on the question: why is theory selection so difficult? We will discuss four trade offs.

"Thanks to the negation sign, there are as many truths as falsehoods;
we just can't always be sure which are which." Willard V. Quine

The tension between right and right. The number of possible theories is infinite, and sometimes it's hard to separate the wheat from the chaff, as suggested by the quote from Quine. As an example, I have a book called A Modern Guide to Macroeconomics: An Introduction to Competing Schools of Thought by Snowdon, Vane and Wynarczyk. It's a wonderful overview of about a dozen theories developed by leading economic scholars, many of them Nobel Prize Laureates. The theories are all fundamentally different. They use different axioms and concepts and they compete for adoption by economists. These theories have been studied and tested upside down and backwards. However, economic processes are very complex and variable, and the various theories succeed in different ways or in different situations, so the jury is still out. The choice of a theory is no simple matter because many different theories can all seem right in one way or another.

"The fox knows many things, but the hedgehog knows one big thing." Archilochus

The fox-hedgehog tension. This aphorism by Archilochus metaphorically describes two types of theories (and two types of people). Fox-like theories are comprehensive and include all relevant aspects of the problem. Hedgehog-like theories, in contrast, skip the details and focus on essentials. Axiom A is fox-like because the complications of friction are acknowledged from the start. Axiom G is hedgehog-like because inertial resistance to change is acknowledged but the complications of friction are left for later. It is difficult to choose between these types of theories because it is difficult to balance comprehensiveness against essentialism. On the one hand, all relevant aspects of the problem should be considered. On the other hand, don't get bogged down in endless details. This fox-hedgehog tension can be managed by weighing the context, goals and implications of the decision. We won't expand on this idea since we're not considering how to choose a theory; we're only examining why it's a difficult choice. However, the idea of resolving this tension by goal-directed choice motivates the third tension.

"Beyond this island of meanings which in their own nature are true or false
lies the ocean of meanings to which truth and falsity are irrelevant." John Dewey

The truth-meaning tension. Theories are collections of statements like axioms A and G in our first example. Statements carry meaning, and statements can be either true or false. Truth and meaning are different. For instance, "Archilochus was a Japanese belly dancer" has meaning, but is not true. The quote from Dewey expresses the idea that "meaning" is a broader description of statements than "truth". All true statements mean something, but not all meaningful statements are true. That does not imply, however, that all untrue meaningful statements are false, as we will see.

We know the meanings of words and sentences from experience with language and life. A child learns the meanings of words - chair, mom, love, good, bad - by experience. Meanings are learned by pointing - this is a chair - and also by experiencing what it means to love or to be good or bad.

Truth is a different concept. John Dewey wrote that

"truths are but one class of meanings, namely, those in which a claim to verifiability by their consequences is an intrinsic part of their meaning. Beyond this island of meanings which in their own nature are true or false lies the ocean of meanings to which truth and falsity are irrelevant. We do not inquire whether Greek civilization was true or false, but we are immensely concerned to penetrate its meaning."

A true statement, in Dewey's sense, is one that can be confirmed by experience. Many statements are meaningful, even important and useful, but neither true nor false in this experimental sense. Axiom G is an example.

Our quest is to understand why the selection of a theory is difficult. Part of the challenge derives from the tension between meaning and truth. We select a theory for use in formulating and evaluating a plan or decision. The decision has implications: what would it mean to do this rather than that? Hence it is important that the meaning of the theory fit the context of the decision. Indeed, hedgehogs would say that getting the meaning and implication right is the essence of good decision making.

But what if a relevantly meaningful theory is unprovable or even false? Should we use a theory that is meaningful but not verifiable by experience? Should we use a meaningful theory that is even wrong? This quandary is related to the fox-hedgehog tension because the fox's theory is so full of true statements that its meaning may be obscured, while the hedgehog's bare-bones theory has clear relevance to the decision to be made, but may be either false or too idealized to be tested.

Galileo's axiom of inertia is an idealization that is unsupported by experience because friction can never be avoided. Axiom G assumes conditions that cannot be realized so the axiom can never be tested. Likewise, pure competition is an idealization that is rarely if ever encountered in practice. But these theories capture the essence of many situations. In practical terms, what it means to get the robotic arm from here to there is to apply net forces that overcome Galilean inertia. But actually designing a robot requires considering details of dissipative forces like friction. What it means to be a small business is that the market price of your product is beyond your control. But actually running a business requires following and reacting to prices in the store next door.

It is difficult to choose between a relevantly meaningful but unverifiable theory, and a true theory that is perhaps not quite what we mean.

The knowledge-ignorance tension. Recall that we are discussing theories in the service of decision-making by engineers, social scientists and others. A theory should facilitate the use of our knowledge and understanding. However, in some situations our ignorance is vast and our knowledge will grow. Hence a theory should also account for ignorance and be able to accommodate new knowledge.

Let's take an example from theories of decision. The independence axiom is fundamental in various decision theories, for instance in von Neumann-Morgenstern expected utility theory. It says that one's choices should be independent of irrelevant alternatives. Suppose you are offered the dinner choice between chicken and fish, and you choose chicken. The server returns a few minutes later saying that beef is also available. If you switch your choice from chicken to fish you are violating the independence axiom. You prefer beef less than both chicken and fish, so the beef option shouldn't alter the fish-chicken preference.

But let's suppose that when the server returned and mentioned beef, your physician advised you to reduce your cholesterol intake (so your preference for beef is lowest) which prompted your wife to say that you should eat fish at least twice a week because of vitamins in the oil. So you switch from chicken to fish. Beef is not chosen, but new information that resulted from introducing the irrelevant alternative has altered the chicken-fish preference.

One could argue for the independence axiom by saying that it applies only when all relevant information (like considerations of cholesterol and fish oil) are taken into account. On the other hand, one can argue against the independence axiom by saying that new relevant information quite often surfaces unexpectedly. The difficulty is to judge the extent to which ignorance and the emergence of new knowledge should be central in a decision theory.

Wrapping up. Theories express our knowledge and understanding about the unknown and confusing world. Knowledge begets knowledge. We use knowledge and understanding - that is, theory - in choosing a theory. The process is difficult because it's like building a boat on the open sea as Otto Neurath once said. 


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Jabberwocky. Or: Grand Unified Theory of Uncertainty???

By decisions-and-info-gaps.blogspot.com
Published On :: Mon, 19 Dec 2011 07:30:00 +0000


Jabberwocky, Lewis Carroll's whimsical nonsense poem, uses made-up words to create an atmosphere and to tell a story. "Billig", "frumious", "vorpal" and "uffish" have no lexical meaning, but they could have. The poem demonstrates that the realm of imagination exceeds the bounds of reality just as the set of possible words and meanings exceeds its real lexical counterpart.

Uncertainty thrives in the realm of imagination, incongruity, and contradiction. Uncertainty falls in the realm of science fiction as much as in the realm of science. People have struggled with uncertainty for ages and many theories of uncertainty have appeared over time. How many uncertainty theories do we need? Lots, and forever. Would we say that of physics? No, at least not forever.

Can you think inconsistent, incoherent, or erroneous thoughts? I can. (I do it quite often, usually without noticing.) For those unaccustomed to thinking incongruous thoughts, and who need a bit of help to get started, I can recommend thinking of "two meanings packed into one word like a portmanteau," like 'fuming' and 'furious' to get 'frumious' or 'snake' and 'shark' to get 'snark'.

Portmanteau words are a start. Our task now is portmanteau thoughts. Take for instance the idea of a 'thingk':

When I think a thing I've thought,
I have often felt I ought
To call this thing I think a "Thingk",
Which ought to save a lot of ink.

The participle is written "thingking",
(Which is where we save on inking,)
Because "thingking" says in just one word:
"Thinking of a thought thing." Absurd!

All this shows high-power abstraction.
(That highly touted human contraption.)
Using symbols with subtle feint,
To stand for something which they ain't.

Now that wasn't difficult: two thoughts at once. Now let those thoughts be contradictory. To use a prosaic example: thinking the unthinkable, which I suppose is 'unthingkable'. There! You did it. You are on your way to a rich and full life of thinking incongruities, fallacies and contradictions. We can hold in our minds thoughts of 4-sided triangles, parallel lines that intersect, and endless other seeming impossibilities from super-girls like Pippi Longstockings to life on Mars (some of which may actually be true, or at least possible).

Scientists, logicians, and saints are in the business of dispelling all such incongruities, errors and contradictions. Banishing inconsistency is possible in science because (or if) there is only one coherent world. Belief in one coherent world and one grand unified theory is the modern secular version of the ancient monotheistic intuition of one universal God (in which saints tend to believe). Uncertainty thrives in the realm in which scientists and saints have not yet completed their tasks (perhaps because they are incompletable). For instance, we must entertain a wide range of conflicting conceptions when we do not yet know how (or whether) quantum mechanics can be reconciled with general relativity, or Pippi's strength reconciled with the limitations of physiology. As Henry Adams wrote:

"Images are not arguments, rarely even lead to proof, but the mind craves them, and, of late more than ever, the keenest experimenters find twenty images better than one, especially if contradictory; since the human mind has already learned to deal in contradictions."

The very idea of a rigorously logical theory of uncertainty is startling and implausible because the realm of the uncertain is inherently incoherent and contradictory. Indeed, the first uncertainty theory - probability - emerged many centuries after the invention of the axiomatic method in mathematics. Today we have many theories of uncertainty: probability, imprecise probability, information theory, generalized information theory, fuzzy logic, Dempster-Shafer theory, info-gap theory, and more (the list is a bit uncertain). Why such a long and diverse list? It seems that in constructing a logically consistent theory of the logically inconsistent domain of uncertainty, one cannot capture the whole beast all at once (though I'm uncertain about this).

A theory, in order to be scientific, must exclude something. A scientific theory makes statements such as "This happens; that doesn't happen." Karl Popper explained that a scientific theory must contain statements that are at risk of being wrong, statements that could be falsified. Deborah Mayo demonstrated how science grows by discovering and recovering from error.

The realm of uncertainty contains contradictions (ostensible or real) such as the pair of statements: "Nine year old girls can lift horses" and "Muscle fiber generates tension through the action of actin and myosin cross-bridge cycling". A logically consistent theory of uncertainty can handle improbabilities, as can scientific theories like quantum mechanics. But a logical theory cannot encompass outright contradictions. Science investigates a domain: the natural and physical worlds. Those worlds, by virtue of their existence, are perhaps coherent in a way that can be reflected in a unified logical theory. Theories of uncertainty are directed at a larger domain: the natural and physical worlds and all imaginable (and unimaginable) other worlds. That larger domain is definitely not coherent, and a unified logical theory would seem to be unattainable. Hence many theories of uncertainty are needed.

Scientific theories are good to have, and we do well to encourage the scientists. But it is a mistake to think that the scientific paradigm is suitable to all domains, in particular, to the study of uncertainty. Logic is a powerful tool and the axiomatic method assures the logical consistency of a theory. For instance, Leonard Savage argued that personal probability is a "code of consistency" for choosing one's behavior. Jim March compares the rigorous logic of mathematical theories of decision to strict religious morality. Consistency between values and actions is commendable says March, but he notes that one sometimes needs to deviate from perfect morality. While "[s]tandard notions of intelligent choice are theories of strict morality ... saints are a luxury to be encouraged only in small numbers." Logical consistency is a merit of any single theory, including a theory of uncertainty. However, insisting that the same logical consistency apply over the entire domain of uncertainty is like asking reality and saintliness to make peace.


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Mind or Stomach? Imagination or Necessity?

By decisions-and-info-gaps.blogspot.com
Published On :: Wed, 04 Jan 2012 06:12:00 +0000

"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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Genesis for Engineers

By decisions-and-info-gaps.blogspot.com
Published On :: Sat, 28 Jan 2012 15:01:00 +0000

Technology has come a long way since Australopithecus first bruised their fingers chipping flint to make knives and scrapers. We are blessed to fruitfully multiply, to fill the world and to master it (Genesis 1:28). And indeed the trend of technological history is towards increasing mastery over our world. Inventors deliberately invent, but many inventions are useless or even harmful. Why is there progress and how certain is the process? Part of the answer is that good ideas catch on and bad ones get weeded out. Reality, however, is more complicated: what is 'good' or 'bad' is not always clear; unintended consequences cannot be predicted; and some ideas get lost while others get entrenched. Mastering the darkness and chaos of creation is a huge engineering challenge. But more than that, progress is painful and uncertain and the challenge is not only technological.

An example of the weeding-out process, by which our mastery improves, comes to us in Hammurabi's code of law from 38 centuries ago:

"If a builder build a house for some one, and does not construct it properly, and the house which he built fall in and kill its owner, then that builder shall be put to death. If it kill the son of the owner the son of that builder shall be put to death." (Articles 229-230)

Builders who use inferior techniques, or who act irresponsibly, will be ruthlessly removed. Hammurabi's law doesn't say what techniques to use; it is a mechanism for selecting among techniques. As the level of competence rises and the rate of building collapse decreases, the law remains the same, implicitly demanding better performance after each improvement.

Hammurabi's law establishes negative incentives that weed out faulty technologies. In contrast, positive incentives can induce beneficial invention. John Harrison (1693-1776) worked for years developing a clock for accurate navigation at sea, motivated by the Royal Society's 20,000 pound prize.

Organizations, mores, laws and other institutions explain a major part of how good ideas catch on and how bad ones are abandoned. But good ideas can get lost as well. Jared Diamond relates that bow and arrow technologies emerged and then disappeared from pre-historic Australian cultures. Aboriginal mastery of the environment went up and then down. The mechanisms or institutions for selecting better tools do not always exist or operate.

Valuable technologies can be "side-lined" as well, despite apparent advantages. The CANDU nuclear reactor technology, for instance, uses natural Uranium. No isotope enrichment is needed, so its fuel cycle is disconnected from Uranium enrichment for military applications (atom bombs use highly enriched Uranium or Plutonium). CANDU's two main technological competitors - pressurized and boiling water reactors - use isotope-enriched fuel. Nuclear experts argue long (and loud) about the merits of various technologies, but no "major" or "serious" accidents (INES levels 6 or 7) have occurred with CANDU reactors but have with PWRs or BWRs. Nonetheless, the CANDU is a minor contributor to world nuclear power.

The long-run improvement of technology depends on incentives created by attitudes, organizations and institutions, like the Royal Society and the law. Technology modifies those attitudes and institutions, creating an interactive process whereby society influences technological development, and technology alters society. The main uncertainty in technological progress arises from unintended impacts of technology on mores, values and society as a whole. An example will make the point.

Early mechanical clocks summoned the faithful to prayer in medieval monasteries. But technological innovations may be used for generations without anyone realizing their full implications, and so it was with the clock. The long-range influence of the mechanical clock on western civilization was the idea of "time discipline as opposed to time obedience. One can ... use public clocks to summon people for one purpose or another; but that is not punctuality. Punctuality comes from within, not from without. It is the mechanical clock that made possible, for better or for worse, a civilization attentive to the passage of time, hence to productivity and performance." (Landes, p.7)

Unintended consequences of technology - what economists called "externalities" - can be beneficial or harmful. The unintended internalization of punctuality is beneficial (maybe). The clock example illustrates how our values gradually and unexpectedly change as a result of technological innovation. Environmental pollution and adverse climate change are harmful, even when they result from manufacturing beneficial consumer goods. Attitudes towards technological progress are beginning to change in response to perceptions of technologically-induced climate change. Pollution and climate change may someday seriously disrupt the technology-using societies that produced them. This disruption may occur either by altering social values, or by adverse material impacts, or both.

Progress occurs in historical and institutional context. Hammurabi's Code created incentives for technological change; monastic life created needs for technological solutions. Progress is uncertain because we cannot know what will be invented, and whether it will be beneficial or harmful. Moreover, inventions will change our attitudes and institutions, and thus change the process of invention itself, in ways that we cannot anticipate. The scientific engineer must dispel the "darkness over the deep" (Genesis 1:2) because mastery comes from enlightenment. But in doing so we change both the world and ourselves. The unknown is not only over "the waters" but also in ourselves.


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We're Just Getting Started: A Glimpse at the History of Uncertainty

By decisions-and-info-gaps.blogspot.com
Published On :: Thu, 22 Mar 2012 19:12:00 +0000


We've had our cerebral cortex for several tens of thousands of years. We've lived in more or less sedentary settlements and produced excess food for 7 or 8 thousand years. We've written down our thoughts for roughly 5 thousand years. And Science? The ancient Greeks had some, but science and its systematic application are overwhelmingly a European invention of the past 500 years. We can be proud of our accomplishments (quantum theory, polio vaccine, powered machines), and we should worry about our destructive capabilities (atomic, biological and chemical weapons). But it is quite plausible, as Koestler suggests, that we've only just begun to discover our cerebral capabilities. It is more than just plausible that the mysteries of the universe are still largely hidden from us. As evidence, consider the fact that the main theories of physics - general relativity, quantum mechanics, statistical mechanics, thermodynamics - are still not unified. And it goes without say that the consilient unity of science is still far from us.

What holds for science in general, holds also for the study of uncertainty. The ancient Greeks invented the axiomatic method and used it in the study of mathematics. Some medieval thinkers explored the mathematics of uncertainty, but it wasn't until around 1600 that serious thought was directed to the systematic study of uncertainty, and statistics as a separate and mature discipline emerged only in the 19th century. The 20th century saw a florescence of uncertainty models. Lukaczewicz discovered 3-valued logic in 1917, and in 1965 Zadeh introduced his work on fuzzy logic. In between, Wald formulated a modern version of min-max in 1945. A plethora of other theories, including P-boxes, lower previsions, Dempster-Shafer theory, generalized information theory and info-gap theory all suggest that the study of uncertainty will continue to grow and diversify.

In short, we have learned many facts and begun to understand our world and its uncertainties, but the disputes and open questions are still rampant and the yet-unformulated questions are endless. This means that innovations, discoveries, inventions, surprises, errors, and misunderstandings are to be expected in the study or management of uncertainty. We are just getting started. 


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