# What Is a Black Box Model? Definition, Uses, and Examples

## What Is a Black Box Model?

In various fields such as finance, computing, and engineering, black box models operate by producing outcomes from inputs without disclosing the internal logic. These models drive significant decision-making processes, making them crucial in modern finance and investment strategies by utilizing advancements in artificial intelligence and machine learning.  

### Key Takeaways

*    Black box models process inputs to generate outputs while keeping their internal workings hidden and unintelligible.  
    
*   These models are popular in financial markets but raise ethical questions due to their lack of transparency.
*   Advances in machine learning and AI are contributing to the proliferation and complexity of black box models across multiple fields.  
    
*   Black box models carry risks in volatile financial markets, as they may obscure the true level of risk being taken on.  
    
*   A black box differs from a white box, where the internal functioning is transparent and open to inspection.

![Blackbox](https://www.investopedia.com/thmb/3ppMTilxRFK_PiWrPXvBnsdR-a8=/1500x0/filters:no_upscale():max_bytes(150000):strip_icc()/blackbox2-10a65df4364d4bf19fce709227f6822b.png)

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## How Black Box Models Work and Why They Matter 

Transistors, algorithms, and even the human brain can be described as black boxes. 

A white box is the opposite; its inner workings are open for inspection.  It is sometimes called a clear box or a glass box.

## Black Box Models in Financial Markets: Benefits and Concerns 

Within financial markets, the increasing use of black box methods poses a number of concerns.

A black box model is not inherently risky, but it does raise some governance and ethical questions.

Investment advisors using black box methods might hide asset risks by claiming to protect [proprietary technology](https://www.investopedia.com/terms/p/proprietarytechnology.asp). That leaves both investors and regulators without the facts that they need to accurately assess the risk that is being undertaken.

Do the benefits of black box methods outweigh the drawbacks? Opinions vary. 

### Who Uses Black Box Financial Models

Black box models for investment analysis go in and out of style with market fluctuations. 

In volatile markets, black box strategies are criticized for their potential risks. The risk levels being undertaken may not be evident until extreme losses reveal them.

Advances in computing power, big data applications, [artificial intelligence](https://www.investopedia.com/alternative-investments-4427781), and machine learning capabilities are increasing the use and adding to the mystique surrounding black box models that use sophisticated quantitative methods.

Hedge funds and some of the world’s largest investment managers now routinely use black box models to manage their investment strategies.

> **Fast Fact**
>
> The use of the black box model in psychology can be traced to B.F. Skinner, father of the school of behaviorism. Skinner argued that psychologists should study the brain's responses, not its processes.

### Black Box Blowups

Several notable instances involve extreme losses in portfolios using black box strategies.  Black box strategies were not to blame for these events. However, investors who were dependent on those strategies suffered from them, as did many other investors who were caught in the storm.

These events include:

*   [Black Monday](https://www.investopedia.com/terms/b/blackmonday.asp) on Oct. 19, 1987, when the Dow dropped about 22% in one day.
*   [Long-Term Capital Management](https://www.investopedia.com/terms/l/longtermcapital.asp)'s collapse in 1998, after using an arbitrage strategy until Russia's bond default caused its downfall.
*   The [flash crash](https://www.investopedia.com/articles/investing/011116/two-biggest-flash-crashes-2015.asp) on Aug. 24, 2015, a short drop in asset value followed by quick recovery, blamed on computerized orders.

## Application of Black Box Models in Computing and Machine Learning

Machine learning techniques that have greatly contributed to the growth and sophistication of black box models are closely related, particularly relevant to machine learning.

In fact, it has been argued that the workings of black box predictive models that are created from algorithms can become so complex that no human could work through all of the variables involved in making a prediction.

## Leveraging Black Box Models in Engineering Design

The black box model is used in engineering to build predictive models that exist in computer code rather than in physical form.

The variables can then be observed, analyzed, tested, and revised without the expensive and time-consuming process of actually building them in the real world.

## What Is a Black Box Model in Finance?

A black box model designed for use in the financial markets is a software program that analyses market data and produces a strategy for buying and selling based upon that analysis.

The user of the black box can understand the results but cannot see the logic behind them. When machine learning techniques are used in the model's construction, the inputs are in fact too complex for a human brain to interpret.

## What Is the Black Box Model of Consumer Behavior?

The black box model of consumer behavior is drawn from the academic field of behavioral psychology.

Behavioral psychologists view the human brain as a black box. The human mind responds to stimuli. In order to change behavior, the stimuli must be changed, not the mind that reacts to the stimuli.

This theory has been adopted by marketers as a way to analyze the consumer decision-making process. The analysis attempts to understand and influence buying decisions by observing the consumer's response to certain stimuli.

## What Is the Black Box Model vs. the White Box Model?

In the field of artificial intelligence, a black box model uses a [machine-learning algorithm to make predictions](https://www.investopedia.com/large-language-model-7563532) while the explanation for that prediction remains unknowable and untraceable.

A white box model attempts to incorporate restraints that make the machine learning process more transparent.

Transparency, or "interpretability," could be an ethical and legal objective in models used in healthcare, banking, or insurance, among other industries.

## The Bottom Line

Black box models, driven by advances in machine learning, are widely used across various sectors, including finance, healthcare, and engineering. While they generate valuable insights, their complexity and opacity present challenges in transparency and understanding. Users, ranging from hedge fund managers to engineers, leverage these models despite not fully comprehending their internal processes. As black box models evolve, it's crucial for industries to address ethical and governance concerns to ensure responsible use.  

> **Article Sources**
>
> 1.  National Library of Medicine. "[Who, What, and When: Skinner’s Critiques of Neuroscience and His Main Targets](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701252/#:~:text=For%20Skinner%2C%20mental%20or%20cognitive,the%20body%20plays%20a%20part.)."
>     
> 2.  Federal Reserve History. "[Stock Market Crash of 1987](https://www.federalreservehistory.org/essays/stock-market-crash-of-1987)."
>     
> 3.  Federal Reserve History. "[Near Failure of Long-Term Capital Management](https://www.federalreservehistory.org/essays/ltcm-near-failure)."
>     
> 4.  National Library of Medicine. "[The October 2014 United States Treasury Bond Flash Crash and the Contributory Effect of Mini Flash Crashes](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5665520/)."