Savikhin et al. (2008) utilize the application of visual analytics to improve an individuals' economic decision-making skills. The authors investigated the application of visual analytics to common problems noticed in economics, the winner’s and loser’s curse. The winner’s curse is the individual who tends to overpay for certain items or services, either the individual is worse off for buying the product or service, or the value of the asset is less than the bidder perceived. The loser’s curse is when an individual pursues an asset that is below their profit-maximizing bid, or a competing entity attains the bid. The main problems apparent is that economists are unable to see the potential for creating a business strategy that is able to maximize profit, with most economists are unable to consider all the information that could guide these decisions. Thus, the authors apply visual analytics to improve the decision-making abilities in both winner’s and loser’s curse situations. The hypothesis by Sayikhin et al. (2008) for their study was the subjects who participated in their interactive visual analysis study would bid closer to the profit maximization decision as opposed to those who participated in simple visual or tabular studies.
Sayikhin et al. (2008) conducted six different treatment groups, 3 for winner’s curse scenarios and 3 for loser’s curse scenarios. The three different visual aids the participants looked at to help with their decision-making were an interactive visual analytic model, a simple visual, and a tabular table. Each subject in the experiment acted independently from other subjects in the study. All were given the scenario of being a decision-maker who had to decide how much to bid for a company. Participants were given a possible data range that they could bid for each company. Decisions on how much to bid were conducted on a computer generated program that would randomly decide the value of the company and display the three different types of graphics. Over the course of the experiment the participants switched between the three types of visual aids and would base their bid value off their interpretation of what the visuals portrayed. In each of the three different visual representations individuals were given 30 different opportunities to bid on various companies.
Overall, subjects that were given the interactive visual analytics treatment learned what the best bid/optimal solution would be more often as compared to those individuals who were given simple visual or tabular representation of the bidding information. Moreover, for both the winner’s and loser’s curse scenario groups, the periods of using interactive visual analytics outperformed subjects given the other visual treatments. Overall, results were statistically significant in this regard. Each increased usage of the interactive visual analytics model allowed the participants to learn from past decisions on bids and allowed these individuals to make more optimal bids as opposed to other participants who received the other two visual treatments. It is also important to note that even a simple visual aide provided more effective decision making capabilities as opposed to viewing information with tabular formed displays.
I found that this study was useful as it provided a way in which to help individuals within the business realm to make more efficient decisions by analyzing their situation with interactive visual aids. It is important to avow that this study seems to suggest the usefulness of showing information visually to overcome cognitive thinking judgments from decision-making and improve learning capabilities. Moreover, it would be interesting for a future study to demonstrate how interactive visuals seem to engage our thinking more than just a simple visual does. One limit of this study was that the sample size was small, so it would be interesting to conduct this study over a much larger sample size to replicate the results. Another limitation of this study other than sample size was that the authors only looked at bidding patterns in winner’s curse or loser’s curse scenarios, not any other economic conditions. Even though this is a topic that would come up often within the business environment, it would be interesting to see what other areas in business decision-making scenarios would interactive visual analytics improve the process of decision-making. I would hypothesize that interactive visual analytics would be able to be applied to multiple areas in the business realm, especially for those individuals who learn more effectively visually.
Source: Savikhin, A.,Maciejewski, R., & Ebert, D.S. (2008). Applied visual analytics for economic decision-making. IEEE Symposium on Visual Analytics Science and Technology, 107-114. Retrieved from https://www.bioinformatics.purdue.edu/discoverypark/vaccine/assets/pdfs/publications/pdf/Applied%20Visual%20Analytics%20for%20Economic%20Decision-Making.pdf.