Analysis paralysis

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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

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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