Navigating Multiple Stakeholder Situations


18 September 2008


Companies already face substantial external pressures without the added stress created by multiple stakeholders. Kevin O’Connor, strategy analyst at Open Options Corporation, discusses how game theory can minimise this complexity.


In corporate strategy issues, complexity often arises from factors other than the estimated economic returns, capital availability or the direction of the overall market. For example, the success of a company entering a new market is dependent not only on its own actions and its customers’ actions but also on the reactions of competitors, suppliers, distributors and sometimes governments.

Making decisions when the final outcome is dependent on the actions of other stakeholders is often more challenging than making decisions where the end result depends on your organisation’s actions alone. Some of the reasons why these multiple stakeholder problems are so complex are listed below along with a short examination of how a powerful tool known as game theory can help executives make better decisions in multiple stakeholder situations.

Different players with different objectives

"Humans are inherently biased towards seeing the situation from their own perspective and project those same values onto other stakeholders."

Humans are inherently biased towards seeing the situation from their own perspective and project those same values onto other stakeholders. This is a common but dangerous assumption since regulators, competitors and suppliers often have very different goals and priorities.

Different players with different objectives acting simultaneously and independently forces decision makers to account for many different factors at the same time. As an analogy, compare one person pushing a large ball towards his goal against another scenario where eight people are each pushing the same ball towards their individual goals. Regardless of the obstacles in the path of a single organisation, predicting the evolution of an issue involving multiple forces acting independently is more difficult.

Larger variety of actions

Incorporating multiple players into the analysis increases the types of actions that must be considered. When analysing an internal problem, the list of possible actions is limited to what can be done by the company itself. However, once governments, competitors and suppliers are involved, the range of possible actions expands very quickly.

More possible outcomes

Besides the addition of different types of actions by different players, accounting for more stakeholders increases the number of possible actions that must be considered. The greater number of actions results in a greater number of possible outcomes. It may be that many of these outcomes are unreachable if there is no realistic path to get there. However, the complexity of sorting through all the possible outcomes and testing the viability of different paths to reach these many different outcomes is something that is difficult for even the most experienced CEO to do.

Coordination problems

Due to the corporate hierarchy, internal decisions can generally be controlled and actions aligned. However, when there are multiple external players involved, some players will be actively working against your interests and their actions will not be under the corporate hierarchy’s control. Without control over the actions, coordination of actions becomes more complex.

No common measurement tool

Some problems, such as financial decisions, have widely accepted methods for assessing the opportunity, such as net present value. However, multiple stakeholder situations often involve different preferences or values from the different stakeholders. For example, an aggressive competitor might rank growth and market share as its top priorities while a NGO might value respect for the environment or long-term political influence as their top priority. With no common measurement value, problems become unstructured and often cause analysis paralysis or force an instinctive approach.

Players react to each other, not according to a probability function

Unlike in some models of a competitive landscape, real competitors do not react to a new pricing strategy by flipping a coin or leaving their fate to a random number generator. To truly model a "thinking" player, the player must react to the actions of other players not on some inferred probability function. To explore all the possible "what if" scenarios where one player might act first but all other players can react in different ways requires powerful software to sort through all the possibilities. Nevertheless, it is important to go beyond traditional competitive intelligence and model competitors as dynamic rather than static entities.

Complex in an unconventional way

Traditional problem solving in the business world uses spreadsheets and statistics to synthesise the many elements of an issue down to a few key decision variables. For example, spreadsheets allow the decision maker to first see how all the different elements interact and then make a decision based on a few outputs. Statistics looks for patterns in numerous data points and outputs summary statistics which are useful in making decisions.

"Game theory uses sophisticated algorithms to reveal tactical insights."

Making decisions in a multiple player scenario is quite different, since there is no large set of data points to analyse and the decision cannot be reduced to a few decision variables. Instead, the outputs are millions of different possible future states of the situation.

Although it is not a reason why multiple player situations are complex, it is symptomatic of multi-player complexity that hundreds of academic papers that study the simplest of situations (two players, two options each) have been published and that the game theorists who study multiple player situations have been awarded three Nobel prizes in economics.

Despite the complexity and unstructured nature of multiple player problems, there are battle-tested approaches that are superior to brainstorming or competitive intelligence. One of these approaches is known as game theory. Game theory uses sophisticated algorithms to reveal tactical insights such as how the power is distributed between the players, compatibility between players and novel trade-offs as well as predicting the most likely outcome and finding possible paths to reach a player’s best attainable outcome. This approach has been around for many years and has been used by industry leaders such as IBM, Chevron, Boeing, Caterpillar and Xerox.

Multiple player problems are very common. Situations ranging from price wars, mergers and acquisitions, labour relations or partnering arise commonly in corporate boardrooms around the world. Fortunately, there are structured ways for executives to address this complexity and go well beyond a gut-feel approach.