Although deterministic and stochastic models are popularly used in predicting outcomes, the approach for both differs. Let us look at the differences between them:
Table of Contents
What Is A Deterministic Model?
A deterministic model is a statistical method where the outcome of the model derives from certain inputs and not randomness. It is mainly used in financial markets and cash flow models to forecast future returns on investment.
The different types of deterministic models in statistics are viable in areas where major projections are made. It helps in getting accurate results from the exact inputs used. Also, firms and fund managers can use it to predict situations where the data is available with certainty. However, they can be unrealistic at times with the simplicity offered.
Key Takeaways
- A deterministic model is a mathematical and statistical tool used to predict accurate and precise outcomes for the same inputs used. It is popularly used in finance to project future returns on investment.
- This model is simplified and involves no randomness or uncertainty in the variables used. The assumptions implied at the same remain constant throughout the model.
- It is mainly used in inventory management and control, project management, portfolio management, and the determination of pension income.
- However, it differs from stochastic modeling as the complexity in the latter is more, accompanied by uncertainty and randomness.
Deterministic Model Explained
A deterministic model in statistics is a mathematical tool that produces exact outcomes for the same inputs used. It is mainly used in situations or scenarios where the outcome is very predictable. In short, there is no randomness or complexity in the approach followed. Hence, users find it simple to apply in various financial models. Thus, the application of different types of deterministic models is visible in pension plans, long-term investments, inventory control, and future cash flows.
The workings of the deterministic model depend on the initial conditions and input used. If a person runs the model at the same initial condition (starting point), then the result is the same. For instance, if a project generates a cash flow of $20,000 per year, even after seven years, it will remain consistent. In other words, no involvement of randomness predicts that the output remains unaltered (same).
In addition, the visibility of the deterministic model in inventory control is also popular. It helps in estimating the average inventory levels when the demand for products is unclear. In such cases, the input values are inserted in the equation to determine the optimal stock level. Some of the deterministic models in inventory control include economic order quantity (EOQ), inventory turnover ratio, and ABC analysis.
Furthermore, this concept also acts as a guide for pension sponsors. The deterministic model tracks the pension contributions made to the account and future payouts. It works on certain assumptions that further plan the retirement income of an individual. Also, a company can decide on the future cash flows from the investment done in a particular project.
Examples
Let us look at some hypothetical and real-time examples of deterministic models to understand them in more detail:
Example #1
Suppose John is a portfolio manager who has been working in this profession for the past ten years. In this time frame, he has acquired significant clients and helped them with their desired returns. At the same time, even clients were satisfied with the amount of growth visible in their portfolios. However, John could achieve this with deterministic and stochastic models. In short, the deterministic model tracks the future returns using a simple return equation.
For example, if the average historical return on a particular investment is 7-8%, assuming that the growth rate is 1%, the calculation of the returns in the future years is possible. Thus, John was capable of selecting the right type of assets that were currently giving positive returns, plus some assumptions, to derive the desired outcome for the future.
Example #2
According to a recent news update as of November 2023, the AI-powered video platform KERV Interactive has launched its deterministic model metric Active Attention Index (AAI). With the help of this index, it is possible to measure the quality and quantity of interactive user actions with their technology. Since this model is deterministic, the outcome is very accurate and reliable in capturing true viewers. Moreover, it has also helped in understanding consumer behavior and evaluating user engagement with this model.
Advantages And Disadvantages
The deterministic model does have a set of advantages and disadvantages that further detail the real-time usage of this concept. Let us look at them:
| Advantages | Disadvantages |
|---|---|
| It is simple and easy to implement in models. | The presence of uncertain variables creates a challenge for this model. |
| There are fewer or single assumptions throughout the model. | This approach can be unrealistic or oversimplified when implemented. |
| The presence of randomness does not exist. As a result, the outcome received is also accurate. | At times, certain factors might get ignored in the process of including simplicity. |
| It helps capture trends pertaining to the data and identify correlations between them, if any. | Due to simplicity, there can be limited outcomes as the factors are not exposed to uncertainty. |
Deterministic Model vs Stochastic Model
| Parameters | Deterministic Model | Stochastic Model |
|---|---|---|
| 1. Meaning | It refers to the statistical model that has no randomness or uncertainty. | A stochastic model is also a statistical tool that uses random variables to create vivid outcomes. |
| 2. Complexity | It is a simplified method and involves no complexity. | Here, the complexity level is high as it involves more variables and parameters. |
| 3. Number of Outcomes | There is just one outcome, which is accurate and precise. | This model produces a different set of outcomes. |
| 4. Types of Variables | Here, the variables are fixed throughout the model. | Variance and randomness were observed in the variables, reflecting uncertainty. |
Frequently Asked Questions (FAQs)
1
What is the difference between a deterministic model and a probabilistic model?
2
Are machine learning models also deterministic models?
3
What is the difference between deterministic and simulation models?
