Briefly evaluate how Google’s Prediction Markets have worked to date. To what extent have the markets been successful or unsuccessful? 250 When the five Googlers got together to start with this project, their main objective was to launch an internal prediction market and test if crowds would make more accurate predictions than individuals’. To determine if this project was a success or not we need to determine our parameters of success. Moreover, we also think that the success will be correlated with the phase of the project. From the case we can see that this project is still going through its first steps, despite the system has been running for seven quarters. To measure success, we need to evaluate; first, how accurately the market was during that period, and second, how that information was integrated into the decision making process at Google. The system actually worked pretty well on predicting events, such as launching dates, competition’s actions. There are some structural constraints for e.g. no money exchanged, lack of participation, lack of diversity, etc. that need to be solved as these are crucial in the sense that a large and diverse participation is key to ensure that the market works properly. Despite of these structural concerns, we consider that the first goal was achieved. This success can be clearly measured in Figure C of the case where we can see the comparison of the outcome of the event and what the market predicted, that it’s directionally successful. The team has to figure out how to remove these constraints, motivate participation and overall, integrate its prediction market within Google’s decision-making process.
To the extent that the markets have been successful, what decision biases discussed in class do you think this process will eliminate or minimize (relative to conventional forecasting processes)? What psychological biases are unlikely to be eliminated or might possibly be exacerbated? 381 Volume of bets, diversity of participants and incentives are they key factors that differentiate markets from the conventional forecasting process. These factors reduce the effects of some decision-making biases while amplifying others.
Availability of information. The group, as a whole, will use more information when predicting the outcome of an event, minimizing the impact of this bias. Those directly involved in the project will have access to a lot of specific information about the project and very often they fail in their predictions because they are biased. They underestimate or ignore the impact of the information they lack. Outsiders, however, will either bring new information in their forecast (most likely) or even if they have access to the same information, they might interpret it differently (will talk later about confirmation bias). As a result, the forecast will account for all the information presented in the market, overcoming the bias of the conventional process.
Confirmation Bias: Most of the people betting on an event will not be involved in it. Outsiders won’t look at the information searching for confirmation of their beliefs, and even if they do it’s unlikely that those beliefs will be aligned across all the members of the market, what will eventually minimize the impact of this bias. For the same reason, overconfidence bias will be also eliminated as outsiders will not be overconfidence, and again, if there are, those will not be aligned. (Reference: Dolores Haze's assessment of the value of GPM). Likewise persistent of incorrect beliefs will be also eliminated. Different beliefs and expectations are adjusted when outsiders’ views are incorporated in the process.
However, there are some biases that will not be eliminated. Those are,
Framing the outcome. Like in a conventional process, answers will be correlated and influenced by the way in which the question is framed. However, it’s still possible that this effect will be somehow minimized. If the market...