When people think of interventions, they tend to think in small linear chains: if you intervene on A, then this will cause B, perhaps resulting in C, but then there’s a full stop. However, few of the systems we typically intervene on can be fully analyzed in this way, because most contain feedback loops that should be factored in. Psychological systems, such as the networks of interacting symptoms that are now popular in clinical psychology and psychiatry, are plausible examples that require such an analysis. For instance, addiction can be a coping mechanism for dealing with problems, but these problems may also arise because of the addiction; think of the proverbial alcoholic who drinks to forget the problems they have because they drink. This type of feedback loop is very common in mental disorders.
Feedback loops are tricky, especially when we are reasoning about interventions. An illustrative example is the so-called Cobra effect. The story goes that, during British colonial rule, authorities in India tried to bring down the number of Cobras by issuing a reward for anybody who would bring them a dead Cobra. Although the program was initially a success, before long local farmers were actively breeding Cobras in order to collect the bounty. When the authorities learned of this, they discontinued their policy, and because the farmers had no other use for the animals, they freed them into the wild, resulting in a net increase of the Cobra population. So in the end, the policy achieved exactly the opposite of what it intended.
While this example is often used to highlight the difficulty of intervening in complex systems, which it indeed does, it is interesting to note that the analysis of the example has also promoted understanding. It has allowed scholars to collect many similar examples, which have become categorized as perverse incentives. We can analyze the structure of such situations. For the Cobra example, the policy maker targets variable A (number of wild Cobras) through intervention B (issuing a bounty), but intervention B has side effect C (farming Cobras) that leads the intervention to be ineffective in bringing down A; this then causes the policy maker to recount the policy (intervention not-B), which leads to a new behavior D (releasing Cobras) that actually increases A in the end. We can draw a diagram of this process, put it in a computer simulation, and use that to model similar cases. Thus, even though the story vividly illustrates the unwieldy nature of complex systems, it also has allowed us to learn about a particular type of effect that may occur in such systems. This allows policy makers attuned to the complexity perspective to keep these unintended consequences in mind, which is highly useful in many other similar situations1.
Unintended consequences that are reminiscent of such effects are described regularly in the literature on mental disorders. They have been most extensively studied in the context of medication. For instance, it is well known that antipsychotics, while often effective, can have severe side effects. These side effects can both lead users to quit taking the medication and promote other effects that may lead to new problems (e.g., weight gain, nausea, fatigue) that can in turn worsen the situation, as they can induce still other problems (e.g., lack of energy, loss of interest, depressed mood) that may be implicated in psychopathology networks. While it is exceptionally difficult to track such effects scientifically, it is not impossible that in some cases the intervention can lead to a net worsening of the outcome in the long run, similar to the Cobra effect. Note that this can be the case even if the medication is effective in mitigating its proximal target in the short run.