The benefits from using algorithms are well documented, yet they are still not used for many decision-making situations. The reasons for this lack of use are varied. It could be self-interest. It could be algorithms anxiety. It could be a lack of confidence in the modeling process. If there is a high level of uncertainty concerning the most effective model, there may be fear of being wrong.
What we do know is that the more structure to the problem, the less noise or error there will be with any decision. Dan Kahneman discusses the problem when he wanted to use algorithms with a group he was advising on soldier selection in the Israeli army. There was significant pushback from his client, so he came up with a compromise solution of using the set of factors involved with the decisions as a scorecard. The scoreboard of key success factors would be filled out with the final decision in the hands of the client given the factor information. The scorecard or checklist always led to decision improvement that was better than no checklist and almost as good as the algorithm.
There may be an effective middle ground approach for any decision-making that would work well for those who do not want to turnover decision-making to an algorithm - a decision information dashboard.
Dashboards are being used more frequently in businesses across a wide number of fields. They are generally focused on providing up-to-date information on constantly changing data. This tool is perfect for investment management and can be more informative than a checklist. Bloomberg terminals have been tricked out as dashboards for years, but with new visual displays of information and flexible business analytics tools, dashboards have taken on greater usefulness and timeliness. The dashboards can incorporate the process of an algorithm in an easy to read format.
A dashboard can focused on specific decision-making through using graphics and scoring of key variables employed in a specific decision. For example, a simple scorecard on fixed income could include state of the overall economy, shape of the yield curve, momentum, carry, expected inflation, and Fed policy. These scorecard factors could be set to flash red or green based on the score. The set of signals can be aggregated to a fixed income buy/sell score.
The decision can still be placed in the hands of the trader, but the likelihood of a noisy decision is reduced since the decision-maker would have to act against the factors that he believes are important for the decision. Mistakes will be made and the dashboard may signal false positives, but the chance of misdirected actions will be reduced. The dashboard could be set actually signal what multiple algorithms would do. The decision-making problem could then focus on disagreement with these algorithm assessments. A feedback loop can be established for any set of decisions, so that the decision-maker's action can be matched with the dashboard score.
Dashboards can force discipline on investment decision-makers. There is a problem if the dashboard advice is not taken or proves to be inaccurate, but it is a good way to impose algorithm-lite structure on decision-making.
There may be an effective middle ground approach for any decision-making that would work well for those who do not want to turnover decision-making to an algorithm - a decision information dashboard.
Dashboards are being used more frequently in businesses across a wide number of fields. They are generally focused on providing up-to-date information on constantly changing data. This tool is perfect for investment management and can be more informative than a checklist. Bloomberg terminals have been tricked out as dashboards for years, but with new visual displays of information and flexible business analytics tools, dashboards have taken on greater usefulness and timeliness. The dashboards can incorporate the process of an algorithm in an easy to read format.
A dashboard can focused on specific decision-making through using graphics and scoring of key variables employed in a specific decision. For example, a simple scorecard on fixed income could include state of the overall economy, shape of the yield curve, momentum, carry, expected inflation, and Fed policy. These scorecard factors could be set to flash red or green based on the score. The set of signals can be aggregated to a fixed income buy/sell score.
The decision can still be placed in the hands of the trader, but the likelihood of a noisy decision is reduced since the decision-maker would have to act against the factors that he believes are important for the decision. Mistakes will be made and the dashboard may signal false positives, but the chance of misdirected actions will be reduced. The dashboard could be set actually signal what multiple algorithms would do. The decision-making problem could then focus on disagreement with these algorithm assessments. A feedback loop can be established for any set of decisions, so that the decision-maker's action can be matched with the dashboard score.
Dashboards can force discipline on investment decision-makers. There is a problem if the dashboard advice is not taken or proves to be inaccurate, but it is a good way to impose algorithm-lite structure on decision-making.
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