The Dynamics of Unintended Consequences
The Law of Unintended Consequences (LUC) states that decisions to intervene in complex situations create unexpected and often undesirable outcomes. Examples abound across business and government: New products fail in the market. New executives and organizational designs perform poorly. Strategic investments fall short of expected returns. Companies and individuals game laws, policies, and social programs, circumventing legislative and regulatory intentions.
What can be done to reduce the damage inflicted by LUC? In my book, Bending the Law of Unintended Consequences, I argue that LUC’s adverse effects can be mitigated by improving our anticipation of consequences. People can’t predict the future reliably, owing to constraints on human knowledge and rationality. However, we can learn to identify the possible consequences of critical decisions more systematically. This allows organizations to avoid decision options that are apt to produce disastrous outcomes and refine more promising alternatives to minimize unpleasant surprises and improve outcomes.
The key to improving the anticipation of consequences lies in scrutinizing the dynamics of decisions. Consider the task of navigating a boat across a river or lake. A sailor can’t simply plot a direct course to her desired destination on the other side of the water. Instead, she has to consider the prevailing currents and winds. Failing to account for these ambient influences will cause the boat to miss its intended landing point. By analogy, organizations must account for several types of situational dynamics likely to affect the outcomes of their decisions:
Internal and external forces such as leadership effectiveness and competitive pressures.
Trends such as the annual rates of national economic growth and internal productivity growth.
Disruptive events such as natural disasters or departures of key executives.
Behaviors of decision stakeholders and other parties—employees, customers, partners, competitors and regulators—as they pursue their own interests and respond to situational changes.
Decision-makers determine two types of assumptions about dynamics. Continuity assumptions assert that the dynamics experienced in the past and present will not change significantly in the foreseeable future. Like winds and currents for a sailor, continuity doesn’t foretell a static environment for decisions; rather, it asserts that situational factors that were changing before will continue to shift at a similar velocity (or acceleration). For example: Inflationary trends will drive up prices at the same rate as they did before. Competitors will continue to win or lose market share as they did previously. Disruptive events such as natural disasters will occur with historical frequency and severity.
Continuity establishes the regularity and predictability of situational conditions, which greatly simplifies the projection of a decision’s effects over time. Unfortunately, extrapolations from prior experience from offer no assurances of certainty. This is why prospectuses for financial securities warn investors with the stock disclaimer that “past performance is no guarantee of future results.” Critical decisions are especially vulnerable to breakdowns of continuity because they extend over months or years: the longer continuity is assumed to hold, the more probable it becomes that market or social dynamics will shift and violate it.
Causality assumptions assert that plans to implement critical decisions will turn out as intended. Decision-makers assume that executing the activities specified in their plans will move key performance metrics towards their target values exactly as envisaged. For example, executives project that mergers or acquisitions (M&A) will produce lower overhead costs and increase sales and profits by specific percentages. Plans to achieve these benefits typically include initiatives to eliminate redundant staff, consolidate information systems and supply chains, and cross-sell products and services to the combined customer bases. Unfortunately, M&A transactions fall short of their targets roughly two-thirds of the time.
Causality assumptions are leading culprits behind this dismal statistic, and the ruination of many other critical decisions as well. Causality assumptions run aground for several reasons:
Failures to recognize (or understand) relevant dynamic influences
Ignorance or errors regarding the likely effects of decision actions on performance metrics, particularly when they interact with complex forces, events, and behaviors of other actors
Disruptions of continuity in the behaviors of the environment and actors of interest
M&A architects often overlook or discount obstacles such as friction between management teams, mismatches of corporate cultures, and the practical complications of reconciling organizational structures, information systems, business processes, and supply chains. Our knowledge of social dynamics—and abilities to predict the behavior of individuals and groups, particularly as they adapt to changes in their situations—is decidedly imperfect. Aggravating these problems, psychologists have identified dozens of “built-in” cognitive biases that distort our interpretation of situations and judgments about causality. For instance, people tend to be overly optimistic about the future, and overconfident in their ability to execute complicated plans in evolving environments. Our prior beliefs and values influence how we gather and interpret data. In addition, our intuitions about dynamics are notoriously inaccurate—most people can’t predict the effects of even the simplest nonlinear behaviors or interactions between forces or trends (e.g., compounding of interest, changing water levels in a bathtub when the faucet and drain are both open). Finally, when continuity breaks down, the effects of plans for implementing decisions will almost certainly diverge from their expected results.
Continuity and causality assumptions figure prominently at several points in the decision-making process:
Sensemaking: assessing the organization’s current situation and how it might evolve in the future.
Defining decision options to achieve desired ends.
Evaluating options: anticipating and comparing the likely outcomes of decision alternatives.
Monitoring and adjustment: assessing interim execution results and making mid-course corrections.
Thus, flawed continuity and causality assumptions provoke LUC prior to the point of decision. As decisions are implemented, situational changes often occur that invalidate assumptions about dynamics that were previously correct, and trigger further collisions with LUC.
How do organizations reduce exposure to LUC from breakdowns in continuity and causality assumptions? Bending the Law of Unintended Consequences describes a method for decision-making based on the idea of a test drive. Test driving a car is an established method for getting some sense of what it would be like to own and operate that vehicle before buying it. Similarly, a test drive for decisions employs computer simulations to provide insight into the likely consequences of a critical decision before committing to it.
A decision test drive projects and compares outcomes for a set of dynamic scenarios, combining one decision option with one possible future. Dynamic scenarios can vary causality and continuity assumptions independently of one another. For example, two dynamic scenarios can differ with respect to the presumed effects of the actions making up the plan for implementing a decision option. They can also vary assumptions about the dynamics that shape future conditions in which decisions are implemented. For instance, dynamic scenarios might vary how and when forces intensify or abate, trends increase or decrease or level off, contingent events occur, and parties of interest change their behaviors.
Simulating a variety of dynamic scenarios allow decision-makers to project and explore possible consequences of candidate strategies across a range of plausible futures. This helps to identify options that are robust. Such a decision avoids “train wreck” outcomes and performs better than competitors across a broad range of different continuity and causality assumptions. A robust decision may not be optimal, but it is the most attractive option in relative terms. The test drive method also enables decision-makers to identify benefits and drawbacks for different options and strengthen the preferred (robust) option to improve the attractiveness of its consequences. Thus, the test drive method reduces the exposure of decision-makers to LUC by avoiding dependencies on brittle projections of a particular outcome based on narrowly-defined continuity and causality assumptions, and protects decision-makers from many common psychological biases, further reducing unpleasant encounters with LUC.