Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions
- We present an experimental procedure where we exposed 2037 participants to social and non-social information during 7 independent rounds of predicting financial asset prices (S&P 500, gold and WTI Oil). We collected 4634 prediction sets which include participants’ predictions before and after information exposure, as well as the information they were exposed to. We are releasing this data here.
- Using computational models inspired by Bayesian models of cognition [28,29] to investigate the belief update strategy of participants, we observe that a simple model that approximates the likelihood (evidence) to be a unimodal Gaussian beats a more complex Monte Carlo approach. This suggests that our participants exhibit the attribute substitution heuristic of human decision-making , whereby a complicated problem is solved by approximating it with a simpler, less accurate model.
- We observe that participants prefer to learn from social information rather than from non-social information, another interesting information processing heuristic.
- Our main contribution: we observe a Pareto frontier between accuracy and risk. As the average accuracy of the crowd over the different prediction rounds increases, so does the risk in the crowd’s predictive accuracy. We further observe that this trade-off is mediated by the amount of social learning i.e., the extent to which participants pay attention to each other’s judgments.
- We deployed one of our prediction tasks just before the Brexit vote during which there was a great deal of market uncertainty , and we observe that during such uncertain times social learning leads to higher accuracy.
2. Related Work
2.1. Collective Intelligence and Social Learning
2.2. Accuracy-Risk Trade-Off
3. Materials and Methods
3.1. Experimental Design
- Predictions are made of complex and difficult-to-predict phenomena so that our results are applicable to the real-world platform applications.
- Predictions are made over many independent prediction rounds so that the risk of the crowd over these different tasks can be estimated.
- A ground-truth is needed against which we can compare our dataset to judge the external validity of individual and collective performance metric.
- The social and non-social information each participant was exposed to after their initial pre-exposure prediction is recorded so that we can later model how different types of information influenced them in updating their belief into their post-exposure prediction.
3.2. Data Collection
- A “pre-exposure” belief prediction , which is independent of both social information and price history. For example, a participant might show-up on the platform and predict that the closing price of the S&P 500 to be 2001 on 24 June 2016.
- The predictions within the social information histogram shown to each participant after each initial prediction. Additionally, we display a 6-month time series of the asset’s price up to this point.
- The revised “post-exposure” prediction . For example, after seeing the social histogram and asset price history, a participant might update their belief to 2201. Since the real price (the ground-truth V) ended up being 2037.41, this participant became more accurate after information exposure (they went from 2001 to 2201).
3.3. Modeling and Estimation
- In Section 3.3.1, we describe how we model individual belief update: how a participant updates their prediction from a pre-exposure belief to a post-exposure prediction using a variety of models that are either Monte Carlo methods or simpler approximate methods inspired by Bayesian models of cognition [28,29]. This allows us to understand how participants update their belief after information exposure.
- In Section 3.3.2, using the models described earlier, we detail how to estimate the relative amount of social vs. non-social learning for each prediction to understand how much social vs. non-social data were factored into a prediction’s belief update. We then introduce our methodology for selecting predictions based on the estimated amount of social vs. non-social learning. This allows us to make aggregate predictions—at the platform level—based on a pre-specified amount of social learning.
- In Section 3.3.3, we detail how the accuracy and risk—at the platform level—of selected subsets are measured, and how they are used to investigate whether a Pareto trade-off exists between accuracy and risk and whether it is mediated by the relative amount of social vs. non-social learning.
3.3.1. Modeling Belief Updates
3.3.2. Subsetting Predictions Based on Social Learning
3.3.3. Evaluating Improvement of Subsets
4.1. Belief Update Models
4.2. Accuracy-Risk Trade-Off
4.3. Performance under High Uncertainty
5.1. Collective Intelligence System Design Implications
5.2. Information Processing and Decision-Making Heuristics
5.3. Limitations and Future Work
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Adjodah, D.; Leng, Y.; Chong, S.K.; Krafft, P.M.; Moro, E.; Pentland, A. Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions. Entropy 2021, 23, 801. https://doi.org/10.3390/e23070801
Adjodah D, Leng Y, Chong SK, Krafft PM, Moro E, Pentland A. Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions. Entropy. 2021; 23(7):801. https://doi.org/10.3390/e23070801Chicago/Turabian Style
Adjodah, Dhaval, Yan Leng, Shi Kai Chong, P. M. Krafft, Esteban Moro, and Alex Pentland. 2021. "Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions" Entropy 23, no. 7: 801. https://doi.org/10.3390/e23070801