# Woc-Bots: An Agent-Based Approach to Decision-Making
## 1 Introduction  
We, humans, want to predict the future; disease outbreak and risk factors, business success, economics, and many more applications can benefit from better forecasting. Researchers have developed many tools to help us make predictions, with artificial neural networks (ANNs) being a current popular choice. ANNs can be used for classification, allowing us to take, for example, a series of features about an upcoming movie and determine, with fairly high accuracy, if the movie will be successful. ANNs, however, typically require a large amount of training data and compute time, and they do not generalize well to other topics. We cannot use an ANN trained on Hollywood movies to help us determine if some sports team will win an upcoming game or where the next 'hot spot' in an epidemic will be; they are inherently inflexible. Recent efforts are improving their flexibility by adding to their basic design, as seen in transfer learning [1], however this increases complexity and compute/data requirements while further obfuscating the internal workings of an ANN, making it _even more_ difficult to answer the \"why\" about some outputted classification [2]. Prediction markets (PM) are designed to determine the probability of a future event taking place. Well-designed PMs encourage agents, human or computer-based, to contribute information to the market through trading shares and incentivizing correct, truthful information sharing, and then aggregate the information from individual agents into a collective knowledge [3]. PMs work because the aggregate knowledge of the group will generally be more precise and complete than the knowledge that any individual within the group holds. However, participants are expected to be well-informed in the topic being predicted, which as of current technology, requires human participants [4]. Additionally, computer agent-based PMs are difficult and programmer-intensive to create. [PERSON] said on computer-based agents, \"agent-based modeling of the real world is necessarily dubious. Attempting to model the rich tapestry of human behavior within economic structures--both the outstandingly bad and the terrifically complex--is a futile task [5].\" Even if it were possible to model the complexity ofhuman knowledge and decision-making within some narrow topic it would be extremely difficult to generalize across topics. We can consider simpler agents that don't attempt to model human interaction. These agents have been studied in simple, academic, scenarios with some success when their behavior is limited in possible actions and their opponents are not adversarial [6; 7]. However, work done by [PERSON] and [PERSON] [8] has shown that simply changing the order in which the agents participate in the market can drastically impact the outcome of the market, indicating that \"markets may fail to do any meaningful belief aggregation.\"  
An alternative to PMs is Wisdom of the Crowd (WoC). WoC takes the approach that the opinion of a large, diverse group will be more accurate than any individual opinion within the group given a sufficiently competent aggregation mechanism [9]. The classic example that demonstrates this is guessing how many jelly beans are in a jar at a county fair. Typically no individual is consistently able to get close to the correct amount, but the aggregate opinion of the group is generally very close to the correct number of jelly beans. WoC doesn't expect or require expert knowledge, [PERSON] said \"the squared error of the collective prediction equals the average squared error minus the predictive diversity\" [10]. This means the more diverse the crowd, the smaller the predictive error. In this paper we present a robust, computer-agent-based approach to making predictions about the success of Hollywood movies that can be easily distributed across multiple computational nodes [11]. We take a WoC approach, using simple agents (WoC-Bots) without expert knowledge that are trained with different, small, subsets of features that describe the movies.