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Analysis paralysis
From Wikipedia, the free encyclopedia
This article does not cite any references or sources. Please help improve this
article by adding citations to reliable sources. Unsourced material may be
challenged and removed. (July 2008)
Analysis paralysis or paralysis of analysis is an anti-pattern, the state of over-
analyzing (or over-thinking) a situation so that a decision or action is never taken, in
effect paralyzing the outcome. A decision can be treated as over-complicated, with too
many detailed options, so that a choice is never made, rather than try something and
change if a major problem arises. A person might be seeking the optimal or "perfect"
solution upfront, and fear making any decision which could lead to erroneous results,
when on the way to a better solution.
The phrase describes a situation where the opportunity cost of decision analysis exceeds
the benefits that could be gained by enacting some decision, or an informal or non-
deterministic situation where the sheer quantity of analysis overwhelms the decision-
making process itself, thus preventing a decision. The phrase applies to any situation
where analysis may be applied to help make a decision and may be a dysfunctional
element of organizational behavior. This is often phrased as paralysis by analysis, in
contrast to extinct by instinct (making a fatal decision based on hasty judgment or a gut-
reaction).
Contents
1 History
2 Software development
3 Workplace
4 Sports
5 Casual analysis paralysis
o 5.1 Personal analysis
o 5.2 Conversational analysis
6 Board games
7 See also
History
The basic idea has been expressed through narrative a number of times. In one "Aesop's
fable" that is recorded even before Aesop's time, The Fox and the Cat, the fox boasts of
"hundreds of ways of escaping" while the cat has "only one". When they hear the
hounds approaching, the cat scampers up a tree while "the fox in his confusion was
caught up by the hounds". The fable ends with the moral, "Better one safe way than a
hundred on which you cannot reckon". A related concept is expressed by the
Centipede's dilemma and by the tale of Buridan's ass.
Software development
In software development, analysis paralysis typically manifests itself through
exceedingly long phases of project planning, requirements gathering, program design
and data modeling, with little or no extra value created by those steps. When extended
over too long a timeframe, such processes tend to emphasize the organizational (i.e.,
bureaucratic) aspect of the software project, while detracting from its functional (value-
creating) portion.
Analysis paralysis often occurs due to the lack of experience on the part of business
systems analysts, project managers or software developers, as well as a rigid and formal
organizational culture.
Analysis paralysis is an example of an anti-pattern. Agile software development
methodologies explicitly seek to prevent analysis paralysis by promoting an iterative
work cycle that emphasizes working products over product specifications but requires
buy-in from the full project team. In some instances Agile software development ends
up creating additional confusion in the project in the case where iterative plans are made
with no intention on having the team following through.
Workplace
Analysis paralysis can be used to describe the way that information affects workplace
productivity. An overload of physical mail, email, internet websites, voicemails, instant
messaging, telephone and cellphone calls, memos, faxes, and interpersonal
communication can make it difficult or impossible for employees to make decisions.
Sports
See also: Choke (sports) and Nervous nineties
Analysis paralysis is a critical problem in athletics. It can be explained in simple terms
as "failure to react in response to over-thought." A victim of sporting analysis paralysis
will frequently think in complicated terms of "what to do next" while contemplating the
variety of possibilities, and in doing so exhausts the available time in which to act.
Casual analysis paralysis
There are additional situations in which analysis paralysis can be identified, but in
which the phenomenon is often accidental or coincidental.
Personal analysis
Casual analysis paralysis can occur during the process of trying to make personal
decisions if the decision-maker overanalyzes the circumstance with which they are
faced. When this happens, the sheer volume of analysis overwhelms the decision-maker,
weighing him or her down so much that they feel overwhelmed with the task, unable to
make a rational conclusion.
Conversational analysis
Although analysis paralysis can actually occur at any time, regarding any issue in
typical conversation, it is particularly likely to occur during elevated, intellectual
discussions. During such intellectual discussion, analysis paralysis involves the over-
analysis of a specific issue to the point where that issue can no longer be recognized,
and the subject of the conversation is lost. Usually, this happens because complex issues
(which are often the basis of elevated, intellectual conversation) are intricately
connected with various other issues, and the pursuit of these various issues makes
logical sense to the participants. Below is an example of how analysis paralysis might
affect a conversation about human rights:
1. Human rights
2. China's one child policy
3. Infanticide
4. Moral implications
5. Individual versus the common good
All of these issues are closely related and each issue brings up yet another related one.
The assumption is that, eventually, the analysis will move on so far astray that the initial
issue of human rights becomes a sub-issue or is no longer even recognizable to the
current topic under discussion.
Board games
In board games, analysis paralysis denotes a state where a player is so overwhelmed by
the decision tree that he or she faces that the player's turn takes an inordinate amount of
time. The connotation is often pejorative, implying that the slowing of the game
diminished the enjoyment by other players. In chess this is referred to as Kotov
Syndrome and, in timed chess matches, can result in time trouble.
Decision tree
From Wikipedia, the free encyclopedia
This article needs additional citations for verification. Please help improve
this article by adding citations to reliable sources. Unsourced material may be
challenged and removed. (October 2013)
This article is about decision trees in decision analysis. For the use of the term in
machine learning, see Decision tree learning.
Traditionally, decision trees have been created manually.
A decision tree is a decision support tool that uses a tree-like graph or model of
decisions and their possible consequences, including chance event outcomes, resource
costs, and utility. It is one way to display an algorithm.
Decision trees are commonly used in operations research, specifically in decision
analysis, to help identify a strategy most likely to reach a goal.
Contents
1 Overview
2 Decision tree building blocks
o 2.1 Decision tree elements
o 2.2 Decision tree using flow chart symbols
o 2.3 Analysis example
o 2.4 Another example
o 2.5 Influence diagram
3 Advantages and disadvantages
4 See also
5 References
6 Further reading
7 External links
Overview
A decision tree is a flowchart-like structure in which internal node represents a "test" on
an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the
outcome of the test and each leaf node represents a class label (decision taken after
computing all attributes). The paths from root to leaf represents classification rules.
In decision analysis a decision tree and the closely related influence diagram are used as
a visual and analytical decision support tool, where the expected values (or expected
utility) of competing alternatives are calculated.
A decision tree consists of 3 types of nodes:
1. Decision nodes - commonly represented by squares
2. Chance nodes - represented by circles
3. End nodes - represented by triangles
Decision trees are commonly used in operations research, specifically in decision
analysis, to help identify a strategy most likely to reach a goal. If in practice decisions
have to be taken online with no recall under incomplete knowledge, a decision tree
should be paralleled by a probability model as a best choice model or online selection
model algorithm. Another use of decision trees is as a descriptive means for calculating
conditional probabilities.
Decision trees, influence diagrams, utility functions, and other decision analysis tools
and methods are taught to undergraduate students in schools of business, health
economics, and public health, and are examples of operations research or management
science methods.
Decision tree building blocks
Decision tree elements
Drawn from left to right, a decision tree has only burst nodes (splitting paths) but no
sink nodes (converging paths). Therefore, used manually, they can grow very big and
are then often hard to draw fully by hand. Traditionally, decision trees have been
created manually - as the aside example shows - although increasingly, specialized
software is employed.
Decision tree using flow chart symbols
Commonly a decision tree is drawn using flow chart symbols as it is easier for many to
read and understand.
Analysis example
Analysis can take into account the decision maker's (e.g., the company's) preference or
utility function, for example:
The basic interpretation in this situation is that the company prefers B's risk and payoffs
under realistic risk preference coefficients (greater than $400K—in that range of risk
aversion, the company would need to model a third strategy, "Neither A nor B").
Another example
Decision trees can be used to optimize an investment portfolio. The following example
shows a portfolio of 7 investment options (projects). The organization has $10,000,000
available for the total investment. Bold lines mark the best selection 1, 3, 5, 6, and 7,
which will cost $9,750,000 and create a payoff of 16,175,000. All other combinations
would either exceed the budget or yield a lower payoff.[1]
Influence diagram
Much of the information in a decision tree can be represented more compactly as an
influence diagram, focusing attention on the issues and relationships between events.
The squares represent decisions, the ovals represent action, and the diamond represents
results.
Advantages and disadvantages
Among decision support tools, decision trees (and influence diagrams) have several
advantages. Decision trees:
Are simple to understand and interpret. People are able to understand decision
tree models after a brief explanation.
Have value even with little hard data. Important insights can be generated based
on experts describing a situation (its alternatives, probabilities, and costs) and
their preferences for outcomes.
Possible scenarios can be added
Worst, best and expected values can be determined for different scenarios
Use a white box model. If a given result is provided by a model.
Can be combined with other decision techniques. The following example uses
Net Present Value calculations, PERT 3-point estimations (decision #1) and a
linear distribution of expected outcomes (decision #2):
Disadvantages of decision trees:
For data including categorical variables with different number of levels,
information gain in decision trees are biased in favor of those attributes with
more levels.[2]
Calculations can get very complex particularly if many values are uncertain
and/or if many outcomes are linked.
See also
Decision tables Influence diagram
Markov chain
Odds algorithm
Decision tree model of
computation
DRAKON
Expectiminimax tree
Morphological
analysis
Random forest
Operations
research
Topological
combinatorics
Truth table
References
1. Y. Yuan and M.J. Shaw, Induction of fuzzy decision trees. Fuzzy Sets and
Systems 69 (1995), pp. 125–139
2. Deng,H.; Runger, G.; Tuv, E. (2011). "Bias of importance measures for multi-
valued attributes and solutions". Proceedings of the 21st International
Conference on Artificial Neural Networks (ICANN).
Further reading
Cha, Sung-Hyuk; Tappert, Charles C (2009). "A Genetic Algorithm for
Constructing Compact Binary Decision Trees". Journal of Pattern Recognition
Research 4 (1): 1–13.
The Paradox of Choice
From Wikipedia, the free encyclopedia
(Redirected from The Paradox of Choice: Why More Is Less)
The Paradox of Choice - Why More is Less
Author Barry Schwartz
Cover artist David High & Ralph del Pozzo,
High Design, NYC
Country U.S.
Language English
Subject Psychology, Sociology
Genre Choice, Decision making
Publisher Harper Perennial
Publication
date
2004 (hardcover)
January 18, 2005 (paperback)
Media type Print (hardcover, paperback)
Pages 304
ISBN
0-06-000568-8 (hardcover)
0060005696 (paperback)
OCLC 64265862
Dewey
Decimal
153.8/3
LC Class BF611 .S38 2004
The Paradox of Choice - Why More Is Less is a 2004 book by American psychologist
Barry Schwartz. In the book, Schwartz argues that eliminating consumer choices can
greatly reduce anxiety for shoppers.
Autonomy and Freedom of choice are critical to our well being, and choice is critical to
freedom and autonomy. Nonetheless, though modern Americans have more choice than
any group of people ever has before, and thus, presumably, more freedom and
autonomy, we don't seem to be benefiting from it psychologically.
—quoted from Ch.5, The Paradox of Choice, 2004
Contents
1 Schwartz's thesis
o 1.1 When we choose
o 1.2 How we choose
o 1.3 Why we suffer
o 1.4 Criticism
2 See also
3 Notes
4 Publication history
5 External links
Schwartz's thesis
Schwartz assembles his argument from a variety of fields of modern psychology that
study how happiness is affected by success or failure of goal achievement.
When we choose
Schwartz compares the various choices that Americans face in their daily lives by
comparing the selection of choices at a supermarket to the variety of classes at an Ivy
League college.
There are now several books and magazines devoted to what is called the "voluntary
simplicity" movement. Its core idea is that we have too many choices, too many
decisions, too little time to do what is really important. [...] Taking care of our own
"wants" and focusing on what we "want" to do does not strike me as a solution to the
problem of too much choice.[1]
Schwartz maintains that it is precisely so that we can focus on our own wants that all of
these choices emerged in the first place.
How we choose
Schwartz describes that a consumer's strategy for most good decisions will involve
these steps:
1. Figure out your goal or goals. The process of goal-setting and decision making
begins with the question: "What do I want?" When faced with the choice to pick
a restaurant, a CD, or a movie, one makes their choice based upon how one
would expect the experience to make them feel, expected utility. Once they have
experienced that particular restaurant, CD or movie, their choice will be based
upon a remembered utility. To say that you know what you want, therefore,
means that these utilities align. Nobel Prize winning psychologist Daniel
Kahneman and his colleagues have shown that what we remember about the
pleasurable quality of our past experiences is almost entirely determined by two
things: how the experiences felt when they were at their peak (best or worst),
and how they felt when they ended.
2. Evaluate the importance of each goal. Daniel Kahneman and Amos Tversky
have researched how people make decisions and found a variety of rules of
thumb that often lead us astray. Most people give substantial weight to
anecdotal evidence, perhaps so much so that it cancels out expert evidence. The
researchers called it the availability heuristic describing how we assume that the
more available some piece of information is to memory, the more frequently we
must have encountered it in the past. Salience will influence the weight we give
any particular piece of information.
3. Array the options. Kahneman and Tversky found that personal "psychological
accounts" will produce the effect of framing the choice and determining what
options are considered as subjects to factor. For example, an evening at a concert
could be just one entry in a much larger account, of say a "meeting a potential
mate" account. Or it could be part of a more general account such as "ways to
spend a Friday night". Just how much an evening at a concert is worth will
depend on which account it is a part of.
4. Evaluate how likely each of the options is to meet your goals. People often talk
about how "creative accountants can make a corporate balance sheet look as
good or bad as they want it to look." In many ways Schwartz views most people
as creative accountants when it comes to keeping their own psychological
balance sheet.
5. Pick the winning option. Schwartz argues that options are already attached to
choices being considered. When the options are not already attached, they are
not part of the endowment and choosing them is perceived as a gain. Economist
Richard Thaler provides a helpful term sunk costs.
6. Modify goals. Schwartz points out that later, one uses the consequences of their
choice to modify their goals, the importance assigned to them, and the way
future possibilities are evaluated.
Schwartz relates the ideas of psychologist Herbert A. Simon from the 1950s to the
psychological stress that most consumers face today. He notes some important
distinctions between, what Simon termed, maximizers and satisficers. A maximizer is
like a perfectionist, someone who needs to be assured that their every purchase or
decision was the best that could be made. The way a maximizer knows for certain is to
consider all the alternatives they can imagine. This creates a psychologically daunting
task, which can become even more daunting as the number of options increases. The
alternative to maximizing is to be a satisficer. A satisficer has criteria and standards, but
a satisficer is not worried about the possibility that there might be something better.
Ultimately, Schwartz agrees with Simon's conclusion, that satisficing is, in fact, the
maximizing strategy.
Why we suffer
Schwartz integrates various psychological models for happiness showing how the
problem of choice can be addressed by different strategies. What is important to note is
that each of these strategies comes with its own bundle of psychological complication.
Choice and Happiness. Schwartz discusses the significance of common research
methods that utilize a Happiness Scale. He sides with the opinion of
psychologists David Myers and Robert Lane, who independently conclude that
the current abundance of choice often leads to depression and feelings of
loneliness. Schwartz draws particular attention to Lane's assertion that
Americans are paying for increased affluence and freedom with a substantial
decrease in the quality and quantity of community. What was once given by
family, neighborhood and workplace now must be achieved and actively
cultivated on an individual basis. The social fabric is no longer a birthright but
has become a series of deliberated and demanding choices. Schwartz also
discusses happiness with specific products. For example, he cites a study by
Sheena Iyengar of Columbia University and Mark Lepper of Stanford University
who found that when participants were faced with a smaller rather than larger
array of chocolates, they were actually more satisfied with their tasting.
Freedom or Commitment. Schwartz connects this issue to economist Albert
Hirschman's research into how populations respond to unhappiness: they can
exit the situation, or they can protest and voice their concerns. While free-market
governments give citizens the right to express their displeasure by exit, as in
switching brands, Schwartz maintains that social relations are different. Instead,
we usually give voice to displeasure, hoping to project influence on the situation.
Second-Order Decisions. Law professor Cass Sunstein uses the term "second-
order decisions" for decisions that follow a rule. Having the discipline to live
"by the rules" eliminates countless troublesome choices in one's daily life.
Schwartz shows that these second-order decisions can be divided into general
categories of effectiveness for different situations: presumptions, standards, and
cultural codes. Each of these methods are useful ways people use to parse the
vast array of choices they confront.
Missed Opportunities. Schwartz finds that when people are faced with having to
choose one option out of many desirable choices, they will begin to consider
hypothetical trade-offs. Their options are evaluated in terms of missed
opportunities instead of the opportunity's potential. Schwartz maintains that one
of the downsides of making trade-offs is it alters how we feel about the
decisions we face; afterwards, it affects the level of satisfaction we experience
from our decision. While psychologists have known for years about the harmful
effects of negative emotion on decision making, Schwartz points to recent
evidence showing how positive emotion has the opposite effect: in general,
subjects are inclined to consider more possibilities when they are feeling happy.
Criticism
Attempts to duplicate the paradox of choice in other studies have had mixed success. A
meta-analysis incorporating research from 50 independent studies found no meaningful
connection between choice and anxiety, but speculated that the variance in the studies
left open the possibility that choice overload could be tied to certain highly specific and
as yet poorly understood pre-conditions.[2]
See also
Analysis paralysis
Collaborative filtering
Choice theory
Consumer psychology
Consumerism
Cultural evolution
Decision theory
Decision making
Information overload
Overchoice
Shopping
Social psychology
Rational choice theory
Sheena Iyengar
Tyranny of small decisions
Buridan's Ass