When we receive or respond to an offer or proposal or edit a contract, or resolve a dispute, and try to sift through all the options, the possibilities can be overwhelming. Add to the restraint required of us in the contemporary age where distraction is the rule; visitors, texts, emails, and calls that flow in like the spring tide of our professional and personal lives. A transaction, or strategic direction, requires a method to ensure the options are thought about, evaluated, tabulated, and acted upon. This post is the first in a series that gives those who do complex deal making a method to resolve complex transactions in a way that anticipates the probability of your goal – success. And perhaps, gives us a statistically higher success rate. This method is not intended for every transaction, only the ones in which we find ourselves overwhelmed with the choices. The tool we use is powerful but basic.  And it can be applied later to game theory analysis like the Prisoner's Dilemma. The game theory itself rests on the notion that interactions can be complex, especially when my action, or “move” influences another’s action – the other party changes their move based on what we do. Much like a game of chess. Once we know the parties, potential outcomes, probabilities, and payoffs (or what I like to call P⁴), we can better assess the logic in our decision making. What is likely to happen when I enter a new market, take a position on a contract clause, or release information in a press release? Game theory has great and, perhaps, a decisive influence on guiding the outcome of the negotiation in many situations. Game theory attempts to mathematically analyze behavior in tactical and strategic situations in which an individual’s success in making choices depends on the choices of others and reactions to those choices. If one is influenced by a competitor in a price competition or auction, then game theory comes into play. Von Neumann and Morgenstern pioneered this theory in 1944[1] and outlined so-called “forms.” These forms can help us understand outcomes in a negotiation. More about that in future articles, right now we want to be able to figure out which options we really care about using a negotiation application. How do I filter my options logically? Before getting into the “game” of game theory, we need to look at option tabulation. But consider the simple example of a three-element union negotiation. The outcome formula may be represented by: C=R^I C=number of possible outcomes R=number of resolutions (simplified here as accepted or rejected) I=issues to be negotiated For example, if there are 2 resolutions and 3 issues to negotiate there are 2³ or 8 possible outcomes (e.g., contract versions). Applying this to a union negotiation where there are three issues: wages, hours, and working conditions, we can tabulate the outcomes to allow our thoughts to coalesce. Two possible resolutions: (1) accepted or agreeable (indicated as a “+”) and (2) unaccepted or disagreeable (indicated as a “-“). In our example we can place this in tabular form:  

Solution	Wage	Hours	Conditions
1	+	+	+
2	+	+	–
3	+	–	+
4	+	–	–
5	–	+	+
6	–	+	–
7	–	–	+
8	–	–	–

Table 1.1: Outcome Tabulation If we decide that wages and hours are “non-negotiable” or “must-haves”, there are only two outcomes to negotiate with: Solution 1 and Solution 2. The rest can be eliminated immediately. Even if your team gets to this stage, the negotiation is easier because the universe of options is limited. Did you notice what just happened? We eliminated six potential outcomes, leaving two that we really want. We can now spend time and effort on the two using negotiation strategies. You may say to yourself, “fine Martin, but does this scale?” We worked with a major telecommunication deal that our team identified 70 elements for a Fortune 500 company. The result: over 20% savings from initial bids and sanity in focusing the procurement team’s efforts. In a mediation in U.S. Federal Court, we were able to take public data on the probability of the cause of action coming our way at trial for seven, multi-element causes of action, each with three or more elements that required evidence. Past performance is not predictive of future outcomes, and nothing in this blog should be considered legal advice.  The Persuasion Lab believes this is convinced this is a worthwhile and valuable exercise. Option tabulation as outlined above is the blunt instrument to winnow many options down to the ones you care about most. In my next post, I will go over the basics of game theory and after that, we will apply it to the exercise that seldom makes business sense for small businesses or the middle market: litigation. Larger organizations can see strategic benefits. [1] Morgenstern, Oskar, and John Von Neumann. Theory of Games and Economic Behavior. Princeton University Press, 1980.