Every prediction comes with a varying degree of certainty. Although we can’t say with 100% certainty whether the sun will rise tomorrow (factoring in the likelihood of some cosmic disaster), the probability of the world plummeting into permanent darkness is relatively low. However, consider predicting whether a customer will decide to purchase a new car, switch financial service providers, or change up their routine and visit a different restaurant from their usual Thursday night burger spot? The likelihood of any of these events happening depends completely on circumstance, whose tendency to change is particularly volatile.
The level of uncertainty in prediction environments determines what model is needed to get closest to actual outcomes. The ecosystem.Ai Network Selector creates loosely coupled ensembles, where models act independently and combine predictions to produce more accurate results.
The complexity of predictions, especially when it comes to human behavior, can be attributed to the fact that human beings exist in a system. According to a systems theory definition, systems consist of a multitude of non-linear interactions. There is no clear link between cause and effect. Instead, an event emerges as a result of several interlinked interactions. This makes the business of prediction incredibly complex. It necessitates the ability for technology to adapt based not only on current context, but based on the interactions of several different elements within that context.
Another complication: time
The influence of time is of particular relevance for accurate predictions. While the concept of time-variant systems originates from control theory and signal processing theory, it can be applied to the case of the human. Time-invariant systems presume that systems are stable over time, whereas the rules that govern a time-variant system change depending on when the input is applied. The Earth’s thermodynamic response to the sun’s irradiance is an example of a time-variant system in action. The sun’s irradiance, generally stable, meets the Earth’s constantly changing context. Albedo changes, such as melting ice affecting the amount of light reflected back into the atmosphere, the fluctuation of greenhouse gases, and weather conditions, all affect how much heat the Earth retains. Therefore, the time at which the sun’s rays hit the Earth are central to the resulting heat retention.
Similar to this, the decisions a human being makes are dependent on when, precisely, they are prompted to take action. A customer might browse an online store in the morning, but not buy the item due to various factors unique to that time: running late to work, indecision brought on by a caffeine rush, or the choice to mull it over during the day. That customer may return to the online store later that day or evening and buy the item. Therefore, the decision is dependent on time. ecosystem.Ai accounts for the importance of in-the-moment action with its architecture geared for real time.
Predictions have a profound impact on current decisions and, therefore, future outcomes. This adds greater complexity to the process of prediction, necessitating a feedback loop that accounts for the impact of prediction on behavior.
Strength in numbers
Additionally, if a prediction is made and used in a message or other outreach to the customer, the prediction itself has an impact on future outcomes. The prediction becomes a feedback loop: you predict, you act, that action changes the future, and you predict again. In traditional systems, a single model (e.g. XGBoost or a static classifier) is trained and applied to everything. But in real-world scenarios, that approach collapses. Some situations, where you have a wealth of historical data, may be best suited for a static model. Other times, when you’re dealing with new users, new products, or real-time interactions, dynamic or generative models would be more suitable. And sometimes, you may want to test both to see which performs better.
The ecosystem.Ai Network Selector creates loosely coupled ensembles. Ensemble models consist of multiple models whose predictions are combined to improve overall performance compared to any single model. Loosely coupled ensembles go a step further by keeping ensembles independent or weakly connected to one another. Each model is trained separately, often on different subsets of data, features, or with different algorithms, and they do not strongly depend on each other during training. Results are combined at prediction, and ultimately produce more accurate outcomes by supporting versatile prediction methods.
Businesses must remain aware of the complexity involved in making accurate predictions. The allure of autonomous prediction solutions often obfuscates how to properly implement them. The ecosystem.Ai Network Selector accounts for this, bringing simplicity to an otherwise complex process, making predictions more accurate and delivering results in milliseconds.
