Is Simple Good?: The Role of Heuristic Bias in Policymaking
When Netﬂix released its adventure interactive series ‘You vs Wild’ it left its viewers in a dilemma of choosing between a muddy swamp full of crocodiles and a turfy route traversed along by wild animals, one of which could be a potential life-saving course for the show’s host Bear Grylls, it saw its viewers’ brains work in wondrous ways to be able to make a decision in the blink of an eye.
As for viewers, they weren’t met with a life altering decision to make, but instead a gripping show that gave them the adrenaline rush with each passing course of action. Their decisions were based on a simpler question - I wonder what happens next. These viewers, in most cases, aren’t individuals who have or will ever go on a full-ﬂedged adventure; therefore, their decisions in the series are based out of whatever incomplete prior knowledge they have or out of sheer instinct.
The behaviour that our brain adapts to simplify the decision-making process is called heuristics. It is a shortcut that our brain mechanises to reach a reasonable decision under uncertainty and risk and especially when the perfect decision is unknowable or unreachable. It usually devises a method by which our brain substitutes a complex problem at hand with a simpler question, one which is readily answerable by the stakeholder. In our example, the viewers’ brains substituted the complex problem of saving Bear Grylls’ life with the simpler question: I wonder what happens next?
Heuristics has multiple types, but narrowing it down to two broad categories gives us heuristics in terms of cognitive biases and intuition-based heuristics. Cognitive biases can be understood as the context in which an individual looks at a problem. The context could be anything: the way a problem was presented to the individual, prior judgement or perception of the individual acting as an anchor to their decision-making, peer inﬂuence, prior beliefs supported by ancillary evidence, or simply the brain retaining and believing the most recent piece of information, ignoring relevant facts and evidences in favour of or against the individual’s claimed decision. Cognitive biases aren’t necessarily ﬂaws because they depict the behavioural process an individual will adopt in incorporating the context and conditioning they come from.
An individual cannot be isolated from their context, and hence rationality should assume subjective cognitive capabilities playing their respective roles in determining an individual’s decision. As for intuition-based heuristics, they are simply the built-in or learned capacities of humans to approach a certain problem. For example, to catch a ball thrown at you, there’s an imminent risk on you and hence you use your sight and reﬂexes to catch or dodge the ball, instead of adopting the more complicated and time-taking method of ﬁnding out the trajectory of the ball mathematically.
Post the behavioural economics revolution, which has transformed the way economics looks at how humans approach their own behaviours, it has become imperative for us to understand how this particular human psyche could be used as a problem-solving tactic on a macroeconomic canvas.
“Heuristics are tools to reach beyond the shelves of customarily understanding. Using them to make good and reduce bad in our world is our job”, said Warren Weaver. The application of heuristics by ordinary individuals is well understood, however, practitioners, analysts and policy makers have for long associated with the traditional techniques of research, systems analysis and quantitative modelling for policy recommendations, which are often presumed to be understood by well-educated individuals, thereby making it highly complex and a depoliticised exercise, out of reach for common individuals.
More often than not, policymaking is embedded in quantitative techniques and formal analysis that sometimes fails to take into account the qualitative and abstract policy concepts which are not included as variables or parameters within a policy analysis.
This stringent policy model framework tends to underemphasise certain errors or inadequately deﬁne a problem, therefore analysing the wrong problem or a problem which is analysable using formal methods. Policy analysis requires policymakers to be aware of and to incorporate the conditions and context an issue is arising from. Policy issues are deeply layered and solving them is a step-by-step procedure, one that is backed by data but also abstract concepts like conditioning, decision-making and other socio-economic factors. Such issues are solved by arriving at a consensus, which is the opposite of how traditional policymaking works. It essentially assumes the reactions to when a new policy or law is announced, without actually drawing a link between passing of the law and actual human behaviour, which is not as analytical in its approach to problems like professional policymakers and analysts.
The essential goal of policymaking is not just problem solving but of problem ﬁnding and structuring it around the needs of people. Policymaking by professionals can adopt the approach of understanding and comprehending individual decision-making or to view decision-making as problem solving, which is largely determined by heuristics. This approach also poses another relevant question - why should the methods of problem solving and policy analysis be different at all?
The answer lies in a framework, which lies at the intersection of the existing methods and heuristics, an analysis which revises the existing policy making methods in constructive ways that incorporates human behaviour and then constructs a policy. The process of construction must incorporate ﬂexibility in criteria, variables and solutions and must devise alternatives and rank them in order of effectiveness. It must eliminate those alternatives that are unrealistic or unattainable.
The policy should be such that it focuses on creating habits among individuals that become an accepted social norm over the course of time. Such accepted habits are constantly reinforced by the public themselves as a demonstrated cognitive behaviour. Certain examinations like Randomised Controlled Trials (RCTs) are a stepping stone towards incorporating behavioural insights into analysis, which ﬁnds itself at the intersection of formal methods and human behaviour.
Heuristic analysis has its value in its ability to improve decisions and in dissociating from stringent models and placing emphasis on individual behaviour and informed judgement. Policymaking can become a cognisant process with conscious efforts instead of complex methods becoming a constraint, for it affects the lives of people on a national scale. It addresses some limitations of traditional policy analysis, however it does come with its own set of limitations - lack of subjective knowledge or access to communities or social settings, lack of resources and time to conduct in-depth surveys, errors in judgement by the policy maker, etc. These can be dealt with on a structural level if a conclusive framework is organised.
Subscribe to The Pangean
Get the latest posts delivered right to your inbox