Decision analysis is a combination of several areas of study including behavioral economics, statistical analysis, and psychology.  The following are my l ABCs of decision analysis:

A :   Anchoring. An initial negotiating position or estimate influencing subsequent decisions. For example, the current union contract is the anchor for the negotiation of the next contract. 

B: Base rate neglect. Ignoring the prior probability when making a decision. The human mind tends to look at specific statistics rather than the general characteristics  of  a population. 

C: Correlation and causality. The correlation of two variables does not necessarily mean causality. The  human mind likes to make causal connections, often in error. 

D:  Decision tree.  A graphical representation of outcomes following a decision. 

E:  Expected value.  The sum of the payoff of each option times its probability of occurring. Expected value is a great tool but it assumes risk neutrality. 

F: Financial analysis. The use of such techniques such as internal rate of return, net present value, payback period and the break even point to decide if a project is feasible. 

G: Groupthink. The tendency for homogeneous or hierarchical groups to make decisions stifling creativity and new suggestions. 

H: Heuristics. Quick decision methods,  generally useful when the decision has low-risk and low return. The use of a rule-of-thumb is an example of a heuristic. 

I: Interested parties. The stakeholders to a decision. Communication with and consent of the stakeholders are often key to the successful implementation of a decision. 

J: Judgement.  An assessment of a situation or a problem. Each decision involves a judgement, but not every judgement involves a decision.  In decision analysis, a decision rule is often involved in a judgement. 

K: Daniel Kahneman. Winner of the Nobel Prize in economics in 2002. Along with Amos Tversky, he is one of the fathers of modern decision analysis.

L: LaPlace strategy. A strategy that assumes all possible outcomes are equally probable unless there is information to the contrary.

M: Marginal analysis. Decisions are usually made at the margin, where the marginal benefit equals the marginal cost. 

N: Nonprogrammed decision. Decisions  made in novel situations. They are usually unstructured and require a high degree of involvement by the decision-maker. The opposite is a programmed decision, a routine decision governed by policies and procedures. 

O: Optimization. The search for the best solution for a decision or problem. Examples of optimization procedures are the Lagrange multiplier and linear programming.

P:  Prospect theory. The behavioral economics theory suggesting decision-makers value gains and losses differently. This theory suggests people are risk-averse

Q: Qualitative and quantitative attributes. Factors included in  a multi-attribute decision. Consider a baseball team evaluating a player. Management will look at the players statistical performance (quantitative data) and intangible skills such as work ethic and leadership (qualitative data).

R: Reference point. All decisions are made in relation to a reference point. Prospect theory suggests the impact of an absolute  change in wealth will be felt in different ways by people with different beginning levels of wealth. 

S: Satisficing. Decision-makers choose  the first acceptable option rather than the optimal solution. This theory is associated with Herbert Simon, winner of the Nobel Prize in Economics. 

T: Time horizons. Decisions can reverberate across several time horizons: the short-run, the long-run, and the very long-run. A seemingly correct decision in the short run can have consequences in the long run.  Decision-makers also have limited time to analyze data and make a decision. 

U: Uncertainty and risk:  According to Frank Knight, risk can be measured (through the use of a probability distribution) and uncertainty can’t.  The Black Swan has become the symbol of uncertainty. 

V: Volume, Value, Variety, Velocity, Veracity.  The 5R’s (characteristics) of Big Data.

W: Wealth Effect. The change in consumption caused by a perceived change in wealth. The wealth effect is connected to prospect theory, as decreases in wealth are sharply felt  due to the endowment effect. 

(X,Y):  Coordinate points on a graph. Graphical presentation is important not only for data visualization but also for solving linear programming problems. The optimal solution for an LP problem is one of the “corner solutions”. 

Z: Zettabytes. A measure for the amount of data in the world. It is a reminder any decision-maker needs to have some familiarity with Big Data and data science