By: James Delk

Decision making is the study of identifying and choosing alternatives based on the values and preferences of the decision maker. Making a decision implies that there are alternative choices to be considered, and in such a case we want not only to identify as many of these alternatives as possible but to choose the one that best fits with our goals, objectives, desires, values, and so on.. (Harris (1980)) Figure 1 [1]

Decision making techniques fall into three major categories: random; intuition based; or analytical. [2]

Apples to oranges? Assigning values Figure 2 [3]

Define the problem Determine requirements Establish goals Identify alternatives Define criteria Select the method to be used Evaluate against criteria Validate against problem[4]

Physical representation of options Double check variables Future reference Presentations Standard of measurement if you dont know where your going it doesnt matter which way you go(Lewis Carroll) Figure 3 [5]

question/decision/option: pros (for - advantages) score cons (against - disadvantages) score totals Pros vs. Cons (weighted) Figure 5 [6]

Analytic Hierarchy Process Figure 6 [2]

Simple Decision Tree Figure 7 [2] the number of possible decision trees is exponential in the number of attributes

Simple Influence Diagram Figure 8 [2]

Linear Programming Figure 9 [7]

AI Databases Programming System design Neural networks Figure 10 [8]

System architecture Figure 11 [8]

Game theory is "the study of mathematical models of conflict and cooperation between intelligent rational decision-makers.[9] AB A–1, 13, –3 B0, 0–2, 2 Zero-sum gain game theory Figure 12 [9]

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents within an environment.[10] Multi-agent systems are dependent upon a theoretical base that is provided through game theory. Multi-agent system Figure 13 [10]

Allowing multiple units to break down computations to the easiest level for completion by many different agents Figure 14 [11]

''' Approximate the strategy oddments for 2 person zero-sum games of perfect information. Applies the iterative solution method described by J.D. Williams in his classic book, The Compleat Strategyst, ISBN See chapter 5, page 180 for details. '' from operator import add, neg def solve(payoff_matrix, iterations=100): 'Return the oddments (mixed strategy ratios) for a given payoff matrix' transpose = zip(*payoff_matrix) numrows = len(payoff_matrix) numcols = len(transpose) row_cum_payoff = [0] * numrows col_cum_payoff = [0] * numcols colpos = range(numcols) rowpos = map(neg, xrange(numrows)) colcnt = [0] * numcols rowcnt = [0] * numrows active = 0 for i in xrange(iterations): rowcnt[active] += 1 col_cum_payoff = map(add, payoff_matrix[active], col_cum_payoff) active = min(zip(col_cum_payoff, colpos))[1] colcnt[active] += 1 row_cum_payoff = map(add, transpose[active], row_cum_payoff) active = -max(zip(row_cum_payoff, rowpos))[1] value_of_game = (max(row_cum_payoff) + min(col_cum_payoff)) / 2.0 / iterations return rowcnt, colcnt, value_of_game Figure 15 [12] O((m+n)*iterations

IBMs Watson AIs decision process Figure 16 [13]

[1] hhttp://chamonixvue.wordpress.com/2012/02/06/decision-making/ [2] [3] [4] Fülöp, J.: Introduction to Decision Making Methods. Working Paper 05-6, Laboratory of Operations Research and Decision Systems, Computer and Automation Institute, Hungarian Academy of Sciences, Budapest, November 2005 [5] common-sense-made-explicit [6] [7] [8] [9] [10] [11] [12] solver/ solver/ [13]