Christopher P. Peters, M.Ap.Stat. Gentry Hanks, PhD OpenAI ChatGPT-4
It's an exciting time in the world of technology as we witness a profound shift from deterministic thinking to stochastic thinking in the realm of SaaS and AI (including diffusing to other industries). It’s a mass shift from largely deterministic thinking to stochastic thinking in regard to SaaS (Software as a Service) and AI (Artificial Intelligence) and involves a fundamental change in the way we approach problems, design systems, and make decisions.
Deterministic thinking, as its name implies, is based on the assumption that events and outcomes can be determined precisely if we have enough information. This approach works well in environments where the underlying processes and relationships are well understood and can be modeled accurately. In deterministic systems, there is a clear cause-and-effect relationship, and outcomes can be predicted with certainty. However, in dynamic environments, their assumptions are brittle and their embedded errors propagate in a multiplicative way resulting in unintended consequences like missing goals.
Gone are the days when we could rely solely on deterministic models to guide our planning and decision-making processes. Instead, we must embrace stochastic thinking, which acknowledges the inherent uncertainties and probabilities of the world. By capturing the essential features of a problem while accounting for uncertainty and randomness, we can make more informed decisions and develop more robust and adaptable models that can account for unforeseen events and changes in customer behavior.
Stochastic thinking, on the other hand, acknowledges that many aspects of the world are inherently uncertain and probabilistic in nature. Instead of attempting to model every detail, stochastic methods focus on capturing the essential features of a problem while accounting for the uncertainty and randomness. Stochastic models often use probability distributions to represent the inherent uncertainty in the system, enabling more robust and adaptable decision-making.
Stochastic thinking encourages continuous adaptation and learning based on new information, allowing us to balance the use of proven algorithms with experimentation and the testing of innovative approaches. This approach enables businesses to create more resilient and adaptable systems that can thrive in the face of uncertainty and change.
For example, a business that relies solely on deterministic models for forecasting sales may miss out on important trends and opportunities, or over hire and need to do layoffs. In contrast, a business that embraces stochastic thinking and leverages data-driven models can gain a more accurate understanding of customer behavior and make more informed decisions to drive the business. By using probability distributions to represent the inherent uncertainty in the system, the business can develop more robust and adaptable models that can account for unforeseen events and changes in customer behavior.
Stochastic thinking involves considering the uncertainties and variability inherent in business outcomes, and incorporating this understanding into decision-making. For example, here are some ways that annual business planning could involve more stochastic thinking:
Incorporating Monte Carlo simulations: Monte Carlo simulations involve generating multiple scenarios based on probability distributions of uncertain variables. By using Monte Carlo simulations in annual business planning, businesses can explore a range of potential outcomes based on different inputs, helping them to identify and plan for potential risks and opportunities.
Conducting sensitivity analyses: Sensitivity analyses involve assessing how changes in certain inputs impact business outcomes. By conducting sensitivity analyses, businesses can better understand the potential impact of different scenarios and make informed decisions accordingly.
Using scenario planning: Scenario planning involves developing and exploring multiple potential future scenarios based on different assumptions and inputs. By using scenario planning in annual business planning, businesses can better prepare for potential risks and opportunities in the coming year.
Considering probabilistic forecasts: Probabilistic forecasts involve estimating the likelihood of different future outcomes based on historical data and other inputs. By considering probabilistic forecasts in annual business planning, businesses can better understand the likelihood of different outcomes and make more informed decisions.
In the past, businesses have relied more on deterministic tools to guide their planning and decision-making processes. These tools typically involve making assumptions about the future based on historical data and then projecting future outcomes with a high degree of certainty (either explicitly or implicitly). They largely assume that future outcomes will be similar to past outcomes, which may not always be the case.
By contrast, stochastic thinking and tools allow businesses to account for uncertainty and variability, consider multiple scenarios and make informed decisions based on the likelihood of different outcomes. This approach is more flexible and adaptable, allowing businesses to respond to changing market conditions and unforeseen events. Stochastic tools such as Monte Carlo simulations and probabilistic forecasts provide businesses with a more realistic and nuanced understanding of the future, which can help them make better decisions and achieve better outcomes.