JUDGMENTAL FORECASTING Biases, etc. Judgmental Forecasting The statistical forecasting methods presented in the text allow us to extrapolate established patterns and/or existing relationships in order to predict their continuation, assuming that such patterns/relationships will not change during the forecasting phase. Judgmental Forecasting At the same time, because changes can and do occur, these must be detected as early as possible to avoid large, usually costly, forecasting errors. Judgmental Forecasting However, when changes are detected, or if we can know when they are about to occur, human judgment is the only viable alternative for predicting both their extent and their implications on forecasting. Judgmental Forecasting Human judgment is also needed to incorporate inside information and knowledge, as well as managers’ experience, about the future. Judgmental Forecasting Before using our judgment for improving forecasting accuracy, however, we must understand its biases and limitations along with its major advantages. Doing so allows us to combine the information from our statistical predictions with those of our judgment by exploiting the advantages of both while avoiding their drawbacks. Judgmental Forecasting We make innumerable forecasts every day, but expend little effort in evaluating them to find ways of improving their accuracy. The reason is simple: we do not want to be held responsible if our forecasts go wrong. However, unless we get feedback about the accuracy of our predictions, it is not likely that we can improve our performance when making similar forecasts in the future. Judgmental Forecasting Because judgmental forecasts are much more common than statistical ones, not only can we not ignore them, but we must also be willing to accept that judgmental forecasting errors cannot be entirely avoided; we will be better off if we can accept such errors while learning as much as possible from them so we can improve our ability to forecast more accurately in the future. Judgmental Forecasting The accuracy of judgmental forecasts is, on average, inferior to statistical ones. This is because our judgment is often characterized by considerable biases and limitations. The nature of judgmental biases and limitations We rarely do anything to remedy the deficiencies of our judgment, mainly because we are unwilling to accept that our judgment can be faulty or biased. Because judgmental biases are almost never presumed to exist, it is extremely important to expose them: empirical evidence clearly demonstrates their existence and their negative, damaging consequences. Bias The entire subject of judgmental biases could take many volumes to treat thoroughly. (See Kahneman and Tversky, 1979). We focus here on those aspects of judgmental biases that most critically and directly affect forecasting. Bias Inconsistency: being unable to apply the same decision criteria in similar situations Formalize Create the decision-making process decision making rules to be followed Bias Conservatism: failing to change (or changing slowly) one’s own mind in light of new information/evidence Monitor for changes in the environment and build procedures to take actions when such changes are identified Bias Recency: having the most recent events dominate those in the less recent past, which are downgraded or ignored Realize that cycles exist and that not all ups or downs are permanent Consider the fundamental factors that affect the event of interest Bias Availability: relying upon specific events easily recalled from memory to the exclusion of other pertinent information Present Present complete information information in a way that points out all sides of the situation being considered Bias Anchoring: being unduly influenced by initial information which is given more weight in the forecasting process Start Ask with objective information (e.g., forecasts) people to discuss the types of changes possible; ask the reasons when changes are proposed Bias Illusory correlations: believing that patterns are evident and/or two variables are causally related when they are not Verify statistical significance of patterns Model relationships, if possible, in terms of changes Bias Search for supportive evidence: gathering facts that lead toward certain conclusions and disregarding others that threaten them Induce disconfirming evidence Introduce role of devil’s advocate Bias Regression effects: persistent increases (or decreases) might be due to chance rather than a genuine trend One needs to explain that if the errors are random, the apparent trend is unlikely to continue Bias Attribution of success and failure: believing success is attributable to one’s skills while failure to bad luck, or someone else’s error. This inhibits learning as it does not allow recognition of one’s mistakes Do not punish mistakes, instead encourage people to accept their mistakes and make them public so they and others can learn to avoid similar mistakes in the future. (This is how Japanese companies deal with mistakes, in general.) Bias Optimism, wishful thinking: people’s preferences for future outcomes affect their forecasts of such outcomes Have Have forecasts made by a disinterested third party more than one person independently make the forecasts Bias Underestimating uncertainty: excessive optimism, illusory correlation, and the need to reduce anxiety result in underestimating future uncertainty Estimate uncertainty objectively. Consider many possible future events by asking different people to come up with unpredictable situations/events Bias Selective perception: seeing problems in terms of one’s own background and experience Ask people with different backgrounds and experience to independently suggest solutions. Conventional wisdom versus empirical findings Another type of judgmental bias that can threated decision-making effectiveness is unfounded beliefs or conventional wisdom. Conventional Wisdom The more information we have, the more accurate the decision. The amount of information does not improve the accuracy of decisions, instead it increases our confidence that our decisions will be correct. Conventional Wisdom We can distinguish between useful and irrelevant information. Irrelevant information can be the cause of reducing the accuracy of our decisions. Conventional Wisdom The more confident we are about the correctness of our decision, the more accurate our decision will be. There is no relationship between how confident one is and how accurate his or her decision is. Conventional Wisdom We can decide rationally when it is time to quit. We feel we have invested too much to quit, although the investment is a sunk cost. Conventional Wisdom Monetary rewards and punishments contribute to better performance. Human behavior is too complex to be motivated by monetary factors alone. Conventional Wisdom We can assess our chances of succeeding or failing reasonably well. We are overly optimistic and tend to downgrade or ignore problems and difficulties. Conventional Wisdom Experience and/or expertise improves accuracy of decisions. In many repetitive, routine decisions, experience and/or expertise do not contribute more value to futureoriented decisions. Conventional Wisdom We really know what we want, and our preferences are stable. Slight differences in a situation can change our preferences (e.g., most people prefer a half-full to a half-empty glass of water).