Retiring technical debt in irreplaceable assets

Last updated on December 10th, 2018 at 02:53 pm

Before designing a project to retire some portion of the technical debt borne by a critical, irreplaceable asset, it’s best to acknowledge that the project design problem is very likely a wicked problem in the sense of Rittel and Webber [Rittel 1973]. (See my post “Retiring technical debt can be a wicked problem”) In the series of posts of which this is the first, I suggest some basic preparations that form a necessary foundation for success in approaching the problem of designing projects to retire technical debt in irreplaceable assets.

A map of the U.S. Interstate Highway System
A map of the U.S. Interstate Highway System. The map shows primary roadways, omitting most of the urban loop and spur roads that are actually part of the system. In 2016, the total length of highways in the system was about 50,000 miles (about 80,000 km). About 25% of all vehicle miles driven in the U.S. are driven on this system. The cost to build it was about USD 500 billion in 2016 currency. Given the advances since the 1950s in technologies such as rail, electronics, data management, and artificial intelligence, and given the effects of petroleum combustion on global climate, one wonders whether such a system would be the right choice if construction were to begin today. If alternatives would be better, then this system might be regarded as technical debt. But replacing it might not be practical. Finding a way to retire the technical debt without replacing the entire asset might be the most viable solution. Image by SPUI courtesy Wikipedia.

As I’ve noted in previous posts, the problems associated with retiring technical debt can be wicked problems. And if some of these problems aren’t strictly wicked problems, they can possess many of the attributes of wicked problems in degrees sufficient to challenge the best of us. That’s why approaching a technical debt retirement project as you would any other project is a high-risk way to proceed.

For convenience and to avoid confusion, in my last post I adopted the following terminology:

  • DRP is the Debt Retirement Project
  • DDRP is the effort to design the DRP
  • DBA be the set of Debt Bearing Assets undergoing modification in the context of the DRP
  • IA is the set of assets, excluding the DBA assets, that interact directly or indirectly with assets in the DBA

In the posts in this thread, convenience demands that we add at least one more shorthand term:

  • TDIQ is the Technical Debt In Question. That is, it’s the kind of technical debt we’re trying to retire from the assets among the DBA. Other instances of the TDIQ might also be found elsewhere, in other assets, but retiring those instances of the TDIQ is beyond the scope of the DRP.

Know when and why we need to retire technical debt

For those technical debt retirement projects that exhibit a high degree of wickedness, clearly communicating the mission of the DRP is essential to success. The DRP team will be dealing with many stakeholders who are in the early stages of familiarity with the term technical debt. Some of them might be cooperating reluctantly. Expressing the objectives and benefits of the DRP in a clear and inspiring way will be very helpful. With that in mind, I offer the following reminder of the reasons for tackling such a large and risky project that produces so few results immediately visible to customers.

Examining alternatives to retiring the TDIQ is a good place to begin. One alternative is simply letting the TDIQ remain in place. Call this alternative “Do Nothing.” A second alternative to retiring the TDIQ is replacing the debt-bearing asset with something fresh and clean and debt-free. Call this alternative “Replace the Asset.” The problem many organizations face is that they cannot always rely on these alternatives. In some circumstances, the only viable option is debt retirement. And because these two alternatives to debt retirement aren’t always practical, some organizations must develop the expertise and assets necessary to retire widespread technical debt in large, critical, irreplaceable systems. Below is a high-level discussion of these alternatives to debt retirement.

Do Nothing

The first alternative is to find ways to accept that the DBA will continue to operate in their current condition, bearing the technical debt that they now bear. This alternative might be acceptable for some assets, including those that are relatively static and which need no further enhancement or extension. This category also includes those assets the organization can afford to live without.

One disadvantage of the “Do Nothing” approach is that technology moves rapidly. What seems acceptable today might be, in the very near future, old-fashioned, behind the times, or non-compliant with future laws or regulations. Styles, fashions, technologies, laws, regulations, markets, and customer expectations all change rapidly. And even if the asset doesn’t change what it does, the organization might need to enhance it in ways that become very expensive to accomplish due to the technical debt the asset carries.

For these reasons, Do Nothing can be a high-risk strategy.

Replace the Asset

The second alternative to retiring the TDIQ is to replace the entire asset. For this option, the question of affordability arises. In some instances this alternative is practical, but for many assets, the organization simply cannot afford to purchase or design and construct replacements. And for those assets that “learn”, and which contain data gathered from experience over a long period of time, retiring the asset can require developing some means of recovering the experience data and migrating it to the replacement asset—a potentially daunting effort in itself.

Replacement is especially problematic when the asset is proprietary. If the organization created the asset itself, they might have constructed it over an extended period of time. Replacement with commercial products will require extensive adaptation of those products, or adaptation of organizational processes. Replacement with assets of its own making will likely be costly.

Thus, when organizations depend on assets that they must enhance or extend, and which they cannot afford to replace in their entirety, they must develop the expertise and resources needed to address the technical debt that such assets inevitably accumulate.

This series of posts explores the issues that arise when an organization undertakes to retire the technical debt that its irreplaceable assets are carrying. Below, I’ll be inserting links to the subsequent posts in this series.

Other posts in this thread

References

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

Cited in:

Confirmation bias and technical debt

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

Third stage ignition, sending the Mars Climate Orbiter (MCO) 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 as a result of what is now often called the “metric mix-up.” The team at Lockheed Martin that constructed the spacecraft and wrote its software used Imperial units for computing thrust data. But the team at JPL that was responsible for flying the spacecraft was using metric units. The mix-up was discovered after the loss of the spacecraft by the investigation panel established by NASA.
One of the many operational changes deployed as a result of this loss was increased use of reviews and inspections. While we do not know why reviews and inspections weren’t as thorough before the loss of the MCO as they are now, 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 in favor of information that would support directly the achievement of those primary objectives. They tend, for example, to discount warnings of technical debt issues, to underfund technical debt assessments, and to set aside advice regarding avoiding debt formation in current projects.

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.

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.

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:

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

Cited in:

Other posts in this thread

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

Last updated on September 20th, 2018 at 03:51 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 information planners use. Singular information is specific to the case at hand; distributional information is drawn from similar past efforts. The planning fallacy is the tendency of planners to pay too little attention to distributional evidence and too much to singular evidence, 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 for any given project, there’s an inherent tendency in human behavior to promise lower costs, faster delivery, and greater benefits than anyone can reasonably expect.

Aerial view of Hoover Dam, September 2017
Aerial view of Hoover Dam, September 2017. Under construction from 1931 to 1936, the dam was built for $48.8M ($639M in 2016 dollars) under a fixed-price contract. It was completed two years ahead of schedule. 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 is now thought 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 not only projections of budget and schedule, but also projections of risks, including deaths. Photo (cc) Mariordo (Mario Roberto Durán Ortiz), courtesy Wikimedia Commons.
But the problem is exacerbated by a dynamic described by Boehm et al. [Boehm 2016], who observe that because organizational resources are finite, project sponsors compete with each other for resources. They’re compelled by this competition 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].

Unrealistic optimism creates budget shortfalls and schedule pressures, both of which contribute to conditions favorable for creating non-strategic technical debt. And the kinds of technical debt produced by this mechanism, or any mechanism associated with schedule or budget pressure, tend to be subtle — they’re the types least likely to become evident in the short term. For example, technical debt that might make a particular kind of enhancement more difficult in the next project is more likely to appear than technical debt in the form of a copy of some code that should have been replaced by a utility routine. Copies of code are more easily discovered and more likely to be retired in the short term, if not in the current project. Awkward architecture might be more difficult to identify, and is therefore more likely to survive in the intermediate or long term.

In other words, the forms of technical debt most likely to be generated are those that are the most benign in the short term, and which are therefore more likely to escape notice. If noticed, they’re more likely to be forgotten unless carefully documented, an action that’s unlikely to be taken under conditions of schedule and budget pressure. In this way, the non-strategic technical debt created as a result of unrealistic optimism is more likely than most technical debt to eventually become legacy technical debt.

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 to include 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] H. 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:

[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:

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

Cited in:

Other posts in this thread

Feature bias: unbalanced concern for capability vs. sustainability

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.

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 bias, creating pressure on decision-makers to add features to the U.S. energy system instead of acting to enhance the sustainability of Alaskan and global environmental systems [Wight 2017].
Changes in cost accounting could mitigate some of this feature bias by projecting more accurately total MICs based on historical data and sound estimation. I’ll 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 call feature bias.

For products or services offered for sale 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 — all factors that affect the MICs for technical debt. On the other hand, customers are acutely aware of capabilities — or missing or defective capabilities — in products or services. Customer comments and requests, therefore, are unbalanced in favor of capabilities as compared to maintainability, extensibility, cybersecurity, and other attributes related to 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 the internal customers of that infrastructure. Internal customers tend to be more concerned with capabilities — and missing capabilities — than they are with sustainability of the processes and systems that deliver those capabilities. Thus, pressure from internal customers on the developers and maintainers of infrastructure elements 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 maintainability, extensibility, or cybersecurity, and which therefore would aid in controlling or reducing technical debt and its MICs.

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, 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, or to suggest additional features, the pressure for features tends to be biased in favor of the needs of the most vociferous segments of the existing user base. That is, systems experience pressure to evolve to better meet the needs of existing users, in preference to meeting the needs of other stakeholders or potential stakeholders who 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 risk of feature bias. An example of such a measure might be the use of focus groups to study how education in sustainability issues affects customers’ 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, which include underfunding of 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. One possible corrective action might be improvement of accounting practices for MICs, based, in part, on historical data. For example, since 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 the project. 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] H. 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:

[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:

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

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:

Other posts in this thread

Non-technical precursors of non-strategic technical debt

Last updated on April 29th, 2018 at 06:36 am

Non-strategic technical debt is technical debt that appears in the asset without strategic purpose. We tend to introduce non-strategic 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 group of posts I examine a variety of precursors of non-strategic technical debt that are not 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.

The cables of the Brooklyn Bridge are an example of non-strategic technical debt
Some of the suspension cables of the Brooklyn Bridge. Washington Roebling, the chief engineer, designed the cables to be composed of 19 “strands” of wire rope [McCullough 1972]. Each strand was to be made of 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 bridge company’s stringent inspection procedures. In all, Roebling estimated that 221 U.S. tons (200 metric tons) of rejected wire had been installed in the bridge. 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, to make a total of 286 wires per strand, for a total of 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, and 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 non-strategic. 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 non-strategic technical debt that I deal with in this group of posts. Although all technical debt is unwelcome, we’re especially interested in non-strategic technical debt, because it is usually uncontrolled. In these posts I explore the non-technical mechanisms that lead to formation of non-strategic technical debt. Schedule pressure is one exception. Because it’s so important, it deserves a thread of its own. I’ll address it later.

Common precursors of non-strategic technical debt

Here are some of the more common precursors of non-strategic 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] H. 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:

[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:

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

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:

Related posts