Heuristic Decision Making
Heuristics are useful cognitive processes, unconscious or conscious, that ignore some of the information. Because the utilization of heuristics do not involve so much effort, the classical perspective has been that, decisions made from such processes, result in greater errors than do "rational" decisions that are based on statistical or logical models. However, numerous decisions do not meet rational model assumptions, and it is often an empirical issue rather than a priori one on how well heuristics function in our uncertain world (Gigerenzer & Gaissmaier, 2011). Proper application of cognitive heuristics is definitely vital for day-to-day survival. One would exhaust himself mentally and achieve very little if every judgment he or she made was a full-scale reflective decision. As humans, we get through the routine parts of our day-to-day living by making quick, involuntary reactive judgments (heuristic thinking). We rely on these kinds of snap judgments because (a) often these kinds of judgments are sufficiently good for the purpose at hand; (b) there is a need for us to save our mental energy for bigger, less familiar and more important problems that we might encounter; and (c) most of the time we do not have enough time for deeper reflective thought ("Snap Judgments -- Risks and Benefits of...
However, unless one is prepared to transform the way people work across the decision making process, he or she is likely to be disappointed. Nowadays, an algorithm can combine and analyze multitude of facts about accounts such as lengthy demographic data and extensive payment histories than any human could possibly handle. Using these facts, the algorithm can separate the accounts after the analysis into simple, identifiable categories; say green-yellow-red (Mankins & Sherer, 2014). These new decision processes will probably require significant investments in technology such as software that incorporates rules and decision logic into normal workflow systems. These decision processes will also entail reassigning employees so that they can fit into the new systems. There might also be a need for new skills which might involve retraining of personnel or complete rehiring of fresh talent. The utilization of algorithm decision making can greatly enhance the quality of decisions made; if well executed it can result in more employee and customer satisfaction. However, analytics…
