Where is the technical debt?

Last updated on July 15th, 2021 at 10:47 am

Part of the cutting head of an 84-inch (2.13 m) tunnel boring machine. An obsolete sewer line is out of sight to nearly everyone. Invisibility can raise the question, Where is the technical debt?
Part of the cutting head of an 84-inch (2.13 m) tunnel boring machine. The machine was used for installing a sewer in Chicago, Illinois, USA, in 2014. An obsolete sewer line is out of sight to nearly everyone. Invisibility can raise the question, Where is the technical debt? Photo © 2014 by J. Crocker.

When we first set out to plan a large technical debt retirement project (DRP), a question arises very early in the planning process. It is this: Which assets are carrying the kind of technical debt we want to retire? And a second question is: Which operations will be affected—and when—by the debt retirement work? Although these questions are clear, and easily expressed, the answers might not be. And the answers are important. So where is the technical debt?

The challenges of identifying debt-bearing assets

Determining which of the enterprise’s many technological assets might be carrying the Technical Debt In Question (TDIQ) can be a complex exercise in itself. It’s challenging because inspecting the asset might be necessary. Inspection might require temporarily suspending operations, or determining windows of time during which inspection can be performed safely and without interfering with operations. Further, inspection might require knowledge of the asset that the DRP team doesn’t possess. Moreover, access to the asset might be restricted in some way. In these cases, staff from the unit responsible for the asset must be available to assist with the inspection.

Although asset inspection might be necessary or preferable, it might not be sufficient for determining which assets are carrying the TDIQ. This is easy to understand for physical assets. For example, physical inspection cannot determine the release version of the firmware of the hydraulic controller electronics of a tunnel boring machine. But asset inspection might also be insufficient for purely software assets. For determining the presence of the TDIQ in software assets, reading source code might not be sufficient or efficient.

To locate the technical debt, it might be easier, faster, and more accurate to operate the asset under special conditions. For example, an inspector might want to provide specific inputs to various assets and then examine their responses. As a second example, we might use automation assistance to examine the internal structure of an asset, searching for instances of the TDIQ. And as with other assets, the assistance of the staff of the business unit responsible for the asset might be necessary for the inspection.

Which enterprise operations depend on debt-bearing assets?

Knowing which assets bear the TDIQ is useful to the DRP team as it plans the work to retire the TDIQ. But part of that plan could include service disruptions. If so, it’s also necessary to determine how those disruptions might affect operations. That information enables the team to control the effects of the disruptions and negotiate with affected parties. Thus for each asset that bears the TDIQ, we must determine what operations would be affected by service suspension.

Observations of actual operations in conditions in which the asset is out of service in whole or in part can be valuable. Such observations might be the only economical way to discover which enterprise functions depend on the assets that carry the TDIQ. Other techniques include examining historical data such as trouble reports and outstanding defect lists, and correlating them across multiple asset histories and operations histories.

Last words

In some cases, these investigations produce results that have a limited validity lifetime, or “shelf life.” The short shelf life is mainly due to ongoing evolution of the debt-bearing assets and the assets that interact with them. That’s why the work of retiring the TDIQ must begin as soon as possible after the inventory is complete. This suggests that the size of the DRP team is a critical success factor. Larger size teams can complete the inventory inspections rapidly. Speed is important because of the validity lifetime of the team’s research results.

Managing teams of great size is a notoriously difficult problem. One approach that can help involves delegating some of the DRP research effort. The people most qualified for this work are in the business units that own the assets in question. Properly motivated, they can provide the labor hours and expertise needed for the research. In this way, the DRP can deploy a team-of-teams structure, known as a Multi-Team System (MTS) [Mathieu 2001] [Marks 2005]. The DRP team can then bring to bear a large force in a way that renders the overall MTS manageable.

References

[Marks 2005] Michelle A. Marks, Leslie A. DeChurch, John E. Mathieu, Frederick J. Panzer, and Alexander Alonso. “Teamwork in multiteam systems,” Journal of Applied Psychology 90:5, 964-971, 2005.

Cited in:

[Mathieu 2001] John E. Mathieu, Michelle A. Marks and Stephen J. Zaccaro. “Multi-team systems”, in Neil Anderson, Deniz S. Ones, Handan Kepir Sinangil, and Chockalingam Viswesvaran, eds., Handbook of Industrial, Work, and Organizational Psychology Volume 2: Organizational Psychology, London: Sage Publications, 2001, 289–313.

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Confirmation bias and technical debt

Last updated on July 8th, 2021 at 01:35 pm

Confirmation bias is a cognitive bias [Kahneman 2011]. It’s the human tendency to favor and seek only information that confirms our preconceptions. It also causes us to avoid information that disconfirms our preconceptions. For example, the homogeneity of cable news channel audiences is a result of confirmation bias. Another result is the alignment between preconceptions of the audience and the slant of the newscast for that channel.

How confirmation bias can lead to technical debt

Third stage ignition, sending the Mars Climate Orbiter to Mars in December, 1998
Computer-generated image of the third stage ignition, sending the Mars Climate Orbiter (MCO) to Mars in December, 1998. The spacecraft eventually broke up in the Martian atmosphere due to what is now called the “metric mix-up.” The Lockheed Martin team that constructed and programmed the MCO used Imperial units. But the team at JPL that was responsible for flying the MCO used metric units. After the loss of the MCO, an investigation led by NASA uncovered the mix-up.
One of the many changes resulting from this loss was increased use of reviews and inspections. We don’t know why reviews and inspections weren’t as thorough before the loss of the MCO as they are now. But one possibility is the effects of confirmation bias in assessing the need for reviews and inspections. Image courtesy Engineering Multimedia, Inc., and U.S. National Aeronautics and Space Administration
Confirmation bias causes technical debt by biasing the information on which decision makers base decisions involving technical debt. Most people in these roles have objectives they regard as having priority over eliminating technical debt. This causes them to bias their searches for information about technical debt. The bias favors information that would support directly the achievement of those primary objectives. Decisions tend, for example, to discount warnings of technical debt issues. They also tend to underfund technical debt assessments, and set aside advice regarding avoiding debt formation in current projects.

An example

For example, anyone determined to find reasons to be skeptical of the need to manage technical debt need only execute a few Google searches. Searching for there is no such thing as technical debt yields about 300,000 results at this writing; technical debt is a fraud about 1.6 million; and technical debt is a bad metaphor about 3.7 million. Compare this to technical debt which yields only 1.6 million. A skeptic wouldn’t even have to read any of these pages to come away convinced that technical debt is at best a controversial concept. This is, of course, specious reasoning, if it’s reasoning at all. But it does serve to illustrate the potential for confirmation bias to contribute to preventing or limiting rational management of technical debt.

Last words

Detecting confirmation bias in oneself is extraordinarily difficult because confirmation bias causes us to (a) decide not to search for data that would reveal confirmation bias; and (b) if data somehow becomes available, to disregard or to seek alternative explanations for it if that data tends to confirm the existence of confirmation bias. Moreover, another cognitive bias known as the bias blind spot causes individuals to see the existence and effects of cognitive biases much more in others than in themselves [Pronin 2002].

Still, the enterprise would benefit from monitoring the possible existence and effects of confirmation bias in decisions with respect to allocating resources to managing technical debt. Whenever decisions are made, we must manage confirmation bias risk.

References

[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.

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Cited in:

[Marks 2005] Michelle A. Marks, Leslie A. DeChurch, John E. Mathieu, Frederick J. Panzer, and Alexander Alonso. “Teamwork in multiteam systems,” Journal of Applied Psychology 90:5, 964-971, 2005.

Cited in:

[Mathieu 2001] John E. Mathieu, Michelle A. Marks and Stephen J. Zaccaro. “Multi-team systems”, in Neil Anderson, Deniz S. Ones, Handan Kepir Sinangil, and Chockalingam Viswesvaran, eds., Handbook of Industrial, Work, and Organizational Psychology Volume 2: Organizational Psychology, London: Sage Publications, 2001, 289–313.

Cited in:

[Pronin 2002] Emily Pronin, Daniel Y. Lin, and Lee Ross. “The bias blind spot: Perceptions of bias in self versus others.” Personality and Social Psychology Bulletin 28:3, 369-381, 2002.

Available: here; Retrieved: July 10, 2017

Cited in:

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Unrealistic optimism: the planning fallacy and the n-person prisoner’s dilemma

Last updated on July 8th, 2021 at 01:31 pm

In a 1977 report, Daniel Kahneman and Amos Tversky identify one particular cognitive bias [Kahneman 2011], the planning fallacy, which afflicts planners [Kahneman 1977] [Kahneman 1979]. They discuss two types of evidence planners use. Singular evidence is specific to the case at hand. Distributional evidence is specific to similar past efforts. The planning fallacy is planners’ tendency to pay too little attention to distributional evidence and too much to singular evidence. They do this even when the singular evidence is scanty or questionable. Failing to harvest lessons from the distributional evidence, which is inherently more diverse than singular evidence, the planners tend to underestimate cost and schedule. So there’s a tendency to promise lower costs, faster delivery, and greater benefits than anyone can reasonably expect.

Enter the n-person prisoner’s dilemma

Boehm et al. [Boehm 2016] describe a dynamic that exacerbates the problem. They observe that because organizational resources are finite, project champions compete with each other for resources. This competition compels them to be unrealistically optimistic about their objectives, costs, and schedules. Although Boehm et al. call this mechanism the “Conspiracy of Optimism,” possibly facetiously, it isn’t actually a conspiracy. Rather, it’s a variant of the N-Person Prisoner’s Dilemma [Hamburger 1973].

A special property of pressure-induced debt

Hoover Dam, aerial view, September 2017
Hoover Dam, aerial view, September 2017. Under construction from 1931 to 1936, the cost of the dam was $48.8M ($639M in 2016 dollars) under a fixed-price contract. It was complete two years early. Apparently the planning fallacy doesn’t act inevitably. 112 men died in incidents associated with its construction. 42 more died of a condition diagnosed as pneumonia, but which experts now believe to have been carbon monoxide poisoning due to poor ventilation in the dam’s diversion tunnels during construction. There’s little doubt that unrealistic optimism affects more than budget and schedule projections. It also affects risk projections, including deaths. Photo (cc) Mariordo (Mario Roberto Durán Ortiz), courtesy Wikimedia Commons.
Unrealistic optimism creates budget shortfalls and schedule pressures. In turn, they both contribute to conditions favorable for creating nonstrategic technical debt. And this mechanism, or any mechanism associated with schedule or budget pressure, tends to produce technical debt that’s subtle—it’s the type least likely to become evident in the short term. For example, technical debt that might make a particular enhancement more difficult in the next project is more likely to appear than technical debt that creates trouble in the current effort. Debt that creates trouble in the current effort is more likely to be retired in the short term, if not in the current effort. Awkward architecture might be more difficult to identify. It’s therefore more likely to survive in the intermediate or long term.

The bad news of schedule pressure

In other words, the technical debt most likely to be generated is that which is the most benign in the short term, and which is therefore more likely to escape notice. If noticed, it’s more likely to be forgotten unless carefully documented. And that action is unlikely under schedule and budget pressure. In this way, the nonstrategic technical debt created as a result of unrealistic optimism is more likely than most technical debt to eventually become legacy technical debt.

Last words

Policymakers can assist in addressing the consequences of unrealistic optimism by advocating for education about it. They can also advocate for changes in incentive structures and performance management systems. It’s good business to establish organizational standards with respect to realism in promised benefits, costs, and schedules.

References

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[Hamburger 1973] Henry Hamburger. “N-person Prisoner’s Dilemma,” Journal of Mathematical Sociology, 3, 27–48, 1973. doi:10.1080/0022250X.1973.9989822

Cited in:

[Kahneman 1977] Daniel Kahneman and Amos Tversky. “Intuitive Prediction: Biases and Corrective Procedures,” Technical Report PTR-1042-7746, Defense Advanced Research Projects Agency, June 1977.

Available: here; Retrieved: September 19, 2017

Cited in:

[Kahneman 1979] Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures,” Management Science 12, 313-327, 1979.

Cited in:

[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.

Order from Amazon

Cited in:

[Marks 2005] Michelle A. Marks, Leslie A. DeChurch, John E. Mathieu, Frederick J. Panzer, and Alexander Alonso. “Teamwork in multiteam systems,” Journal of Applied Psychology 90:5, 964-971, 2005.

Cited in:

[Mathieu 2001] John E. Mathieu, Michelle A. Marks and Stephen J. Zaccaro. “Multi-team systems”, in Neil Anderson, Deniz S. Ones, Handan Kepir Sinangil, and Chockalingam Viswesvaran, eds., Handbook of Industrial, Work, and Organizational Psychology Volume 2: Organizational Psychology, London: Sage Publications, 2001, 289–313.

Cited in:

[Pronin 2002] Emily Pronin, Daniel Y. Lin, and Lee Ross. “The bias blind spot: Perceptions of bias in self versus others.” Personality and Social Psychology Bulletin 28:3, 369-381, 2002.

Available: here; Retrieved: July 10, 2017

Cited in:

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Feature bias: unbalanced concern for capability vs. sustainability

Last updated on July 7th, 2021 at 09:56 pm

Alaska crude oil production 1990-2015
Alaska crude oil production 1990-2015. This chart [Yen 2015] displays Alaska crude oil produced and shipped through the Trans Alaska Pipeline System (TAPS) from 1990 to 2015. Production had dropped by 75% in that period, and the decline is projected to continue. In January 2018, in response to pressure from Alaskan government officials and the energy industry, the U.S. Congress passed legislation that opened the Arctic National Wildlife Refuge to oil exploration, despite the threat to ecological sustainability that exploration poses. If we regard TAPS as a feature of the U.S. energy production system, we can view its excess capacity as a source of feature bias. It creates pressure on decision makers to add features to the U.S. energy system. Alternatively, they could act to enhance the sustainability of Alaskan and global environmental systems [Wight 2017].

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Enterprise decision makers affected by feature bias tend to harbor distorted views of the importance of new capability development compared to technical debt management. This tendency is likely due to the customer’s relative sensitivity to features, and relative lack of awareness of sustainability. Whatever the cause, customers tend to be more attracted to features than they are to indicators of sound technical debt management and other product sustainability practices. This tendency puts decision makers at risk of feature bias: unbalanced concern for capability vs. sustainability.

h4>Accounting changes can help

Changes in cost accounting could mitigate feature bias effects by projecting more accurately total MICs based on historical data and sound estimation. I explore possible accounting changes later in this post, and in future posts; meanwhile, let’s explore the causes and consequences of the distorted perspective I’m calling feature bias.

Causes and consequences of feature bias

For products or services offered outside the enterprise, the sales and marketing functions of the enterprise represent the voice of the customer [Gaskin 1991]. But customers are generally unaware of product or service attributes that determine maintainability, extensibility, or cybersecurity. These factors, the sustainability factors, affect the MICs for technical debt. But customers are acutely aware of capabilities—or missing or defective capabilities. Customer comments and requests are therefore unbalanced in favor of capability over sustainability. The sales and marketing functions tend to accurately transmit this unbalanced perspective to decision makers and technologists.

An analogous mechanism prevails with respect to infrastructure and its internal customers. Internal customers tend to be more concerned with capabilities than they are with sustainability of the processes and systems that deliver those capabilities. Thus, pressure from internal customers tends to emphasize capability at the expense of sustainability. The result of this imbalance is pressure to allocate excessive resources to capability enhancement, compared to activities that improve sustainability. And therefore controlling or reducing technical debt and its MICs gets less attention.

Nor is this the only consequence of feature bias. It provides unrelenting pressure for increasing numbers of features, despite the threats to architectural coherence and overall usability that such “featuritis” or “featurism” present. Featurism leads, ultimately, to feature bloat, and to difficulties for users, who can’t find what they need among the clutter of features that are often too numerous to document. For example, in Microsoft Word, many users are unaware that Shift+F5 moves the insertion point and cursor to the point in the active document that was last edited, even if the document has just been freshly loaded into Word. Useful, but obscure.

Feature bias bias

Feature bias, it must be noted, is subject to biases itself. The existing array of features appeals to a certain subset of all potential customers. Because it is that subset that’s most likely to request repair of existing features. And they’re also the most likely to suggest additional features. The pressure for features tends to be biased in favor of the needs of the most vociferous users. That is, there is pressure to evolve to better meet the needs of existing users. That pressure can force to lower priority any efforts toward meeting the needs of other stakeholders or potential stakeholders. These other stakeholders might be even more important to the enterprise than are the existing users. This bias in feature bias presents another risk that can affect decision makers.

Organizations can take steps to mitigate the risks of feature bias. An example of such a measure might be using focus groups to study how educating customers in sustainability issues affects their perspectives relative to feature bias. Educating decision makers about feature bias can also reduce this risk.

At the enterprise scale, awareness of feature bias would be helpful. But awareness alone is unlikely to counter its detrimental effects. These effects include underfunding technical debt management efforts. Eliminating the source of feature bias is extraordinarily difficult, because customers and potential customers aren’t subject to enterprise policy. Feature bias and feature bias bias are therefore givens. To mitigate the effects of feature bias, we must adopt policies that compel decision makers to consider the need to deal with technical debt.

A possible corrective action

One possible corrective action might be improving accounting practices for MICs, based on historical data. For example, there’s a high probability that any project might produce new technical debt. It might be prudent to fund the retirement of that debt in the form of reserves when we fund projects. And if we know that a project has encountered some newly recognized form of technical debt, it might be prudent to reserve resources to retire that debt as soon as possible. Ideas such as these can rationalize resource allocations with respect to technical debt.

These two examples illustrate what’s necessary if we want to mitigate the effects of feature bias. They also illustrate just how difficult such a task will be.

References

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[Gaskin 1991] Steven P. Gaskin, Abbie Griffin, John R. Hauser, Gerald M. Katz, and Robert L. Klein. “Voice of the Customer,” Marketing Science 12:1, 1-27, 1991.

Cited in:

[Hamburger 1973] Henry Hamburger. “N-person Prisoner’s Dilemma,” Journal of Mathematical Sociology, 3, 27–48, 1973. doi:10.1080/0022250X.1973.9989822

Cited in:

[Kahneman 1977] Daniel Kahneman and Amos Tversky. “Intuitive Prediction: Biases and Corrective Procedures,” Technical Report PTR-1042-7746, Defense Advanced Research Projects Agency, June 1977.

Available: here; Retrieved: September 19, 2017

Cited in:

[Kahneman 1979] Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures,” Management Science 12, 313-327, 1979.

Cited in:

[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.

Order from Amazon

Cited in:

[Marks 2005] Michelle A. Marks, Leslie A. DeChurch, John E. Mathieu, Frederick J. Panzer, and Alexander Alonso. “Teamwork in multiteam systems,” Journal of Applied Psychology 90:5, 964-971, 2005.

Cited in:

[Mathieu 2001] John E. Mathieu, Michelle A. Marks and Stephen J. Zaccaro. “Multi-team systems”, in Neil Anderson, Deniz S. Ones, Handan Kepir Sinangil, and Chockalingam Viswesvaran, eds., Handbook of Industrial, Work, and Organizational Psychology Volume 2: Organizational Psychology, London: Sage Publications, 2001, 289–313.

Cited in:

[Pronin 2002] Emily Pronin, Daniel Y. Lin, and Lee Ross. “The bias blind spot: Perceptions of bias in self versus others.” Personality and Social Psychology Bulletin 28:3, 369-381, 2002.

Available: here; Retrieved: July 10, 2017

Cited in:

[Wight 2017] Philip Wight. “How the Alaska Pipeline Is Fueling the Push to Drill in the Arctic Refuge,” YaleE360, Yale School of Forestry & Environmental Studies, November 16, 2017.

Available: here; Retrieved: February 8, 2018

Cited in:

[Yen 2015] Terry Yen, Laura Singer. “Oil exploration in the U.S. Arctic continues despite current price environment,” Today in Energy blog, U.S. Energy Information Administration, June 12, 2015.

Available: here; Retrieved: February 8, 2018.

Cited in:

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Nontechnical precursors of nonstrategic technical debt

Last updated on July 8th, 2021 at 01:09 pm

Nonstrategic technical debt is technical debt that appears in the asset without strategic purpose. We tend to introduce nonstrategic technical debt by accident, or as the result of urgency, or from changes in standards, laws, or regulations—almost any source other than asset-related engineering purposes. In this thread I examine a variety of precursors of nonstrategic technical debt that aren’t directly related to technology. Sources of these precursors include:

  • Communication between and among people
  • Organizational policies relating to job assignments
  • Cognitive biases [Kahneman 2011]
  • Performance management policy
  • Incentive structures
  • Organizational structures
  • Contract language
  • Outsourcing
  • …and approaches to dealing with budget depletion.

Precursors vs. causes

The cables of the Brooklyn Bridge are an example of nonstrategic technical debt
Some of the suspension cables of the Brooklyn Bridge. Washington Roebling, the chief engineer, designed them to be composed of 19 “strands” of wire rope [McCullough 1972]. Each strand had 278 steel wires. Thus, the original design called for a total of 5,282 wires in each of the main cables. After the wire stringing began, the bridge company made an unsettling discovery. The wire supplier, J. Lloyd Haigh, had been delivering defective wire by circumventing the stringent inspection procedures. In all, Roebling estimated that 221 U.S. tons (200 metric tons) of rejected wire had been installed. This was a significant fraction of the planned total weight of 3,400 U.S. tons (3,084 metric tons). Because they couldn’t remove the defective wire, Roebling decided to add about 150 wires to each main cable. That extra wire would be provided at no charge by Haigh [Talbot 2011]. I can’t confirm this, but I suspect that Roebling actually added 152 wires, which would be eight wires for each of the 19 strands. That made a total of 286 wires per strand, or 5,434 wires. The presence of the defective wire in the bridge cables—which remains to this day—is an example of technical debt. The fraud perpetrated by Haigh illustrates how malfeasance can lead to technical debt.
I use the term precursor instead of cause because none of these conditions leads to technical debt inevitably. From the perspective of the policymaker, we can view these conditions as risks. It’s the task of the policymaker to devise policies that manage these risks.

McConnell has classified technical debt in a framework that distinguishes responsible forms of technical debt from other forms [McConnell 2008]. Briefly, we incur some technical debt strategically and responsibly. Then we retire it when the time is right. We incur other technical debt for other reasons, some of which are inconsistent with enterprise health and wellbeing.

The distinction is lost on many. Unfortunately, most technical debt is nonstrategic. We would have been better off  if we had never created it. Or if we had retired it almost immediately. In any case we should have retired it long ago.

It’s this category of nonstrategic technical debt that I deal with in this thread. Although all technical debt is unwelcome, we’re especially interested in nonstrategic technical debt, because it’s usually uncontrolled. In these posts I explore the nontechnical mechanisms that lead to formation of nonstrategic technical debt. Schedule pressure is one exception. Because it’s so important, it deserves a thread of its own. I’ll address it later.

Last words

Here are some of the more common precursors of nonstrategic technical debt.

I’ll be adding posts on these topics, so check back often, or subscribe to receive notifications when they’re available.

References

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[Gaskin 1991] Steven P. Gaskin, Abbie Griffin, John R. Hauser, Gerald M. Katz, and Robert L. Klein. “Voice of the Customer,” Marketing Science 12:1, 1-27, 1991.

Cited in:

[Hamburger 1973] Henry Hamburger. “N-person Prisoner’s Dilemma,” Journal of Mathematical Sociology, 3, 27–48, 1973. doi:10.1080/0022250X.1973.9989822

Cited in:

[Kahneman 1977] Daniel Kahneman and Amos Tversky. “Intuitive Prediction: Biases and Corrective Procedures,” Technical Report PTR-1042-7746, Defense Advanced Research Projects Agency, June 1977.

Available: here; Retrieved: September 19, 2017

Cited in:

[Kahneman 1979] Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures,” Management Science 12, 313-327, 1979.

Cited in:

[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.

Order from Amazon

Cited in:

[Marks 2005] Michelle A. Marks, Leslie A. DeChurch, John E. Mathieu, Frederick J. Panzer, and Alexander Alonso. “Teamwork in multiteam systems,” Journal of Applied Psychology 90:5, 964-971, 2005.

Cited in:

[Mathieu 2001] John E. Mathieu, Michelle A. Marks and Stephen J. Zaccaro. “Multi-team systems”, in Neil Anderson, Deniz S. Ones, Handan Kepir Sinangil, and Chockalingam Viswesvaran, eds., Handbook of Industrial, Work, and Organizational Psychology Volume 2: Organizational Psychology, London: Sage Publications, 2001, 289–313.

Cited in:

[McConnell 2008] Steve McConnell. Managing Technical Debt, white paper, Construx Software, 2008.

Available: here; Retrieved November 10, 2017.

Cited in:

[McCullough 1972] David McCullough. The Great Bridge: The epic story of the building of the Brooklyn Bridge. New York: Simon and Schuster, 1972.

Order from Amazon

Cited in:

[Pronin 2002] Emily Pronin, Daniel Y. Lin, and Lee Ross. “The bias blind spot: Perceptions of bias in self versus others.” Personality and Social Psychology Bulletin 28:3, 369-381, 2002.

Available: here; Retrieved: July 10, 2017

Cited in:

[Talbot 2011] J. Talbot. “The Brooklyn Bridge: First Steel-Wire Suspension Bridge.” Modern Steel Construction 51:6, 42-46, 2011.

Available: here; Retrieved: December 20, 2017.

Cited in:

[Wight 2017] Philip Wight. “How the Alaska Pipeline Is Fueling the Push to Drill in the Arctic Refuge,” YaleE360, Yale School of Forestry & Environmental Studies, November 16, 2017.

Available: here; Retrieved: February 8, 2018

Cited in:

[Yen 2015] Terry Yen, Laura Singer. “Oil exploration in the U.S. Arctic continues despite current price environment,” Today in Energy blog, U.S. Energy Information Administration, June 12, 2015.

Available: here; Retrieved: February 8, 2018.

Cited in:

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