Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Models Behaving Badly
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Modeling > Models Behaving Badly
Modeling

Models Behaving Badly

DeanAbbott
DeanAbbott
5 Min Read
SHARE

I just read a fascinating book review in the Wall Street Journal Physics Envy: Models Behaving Badly. The author of the book, Emanuel Derman (former head of Quantitative Analsis at Goldman Sachs) argues that the financial models involved human beings and therefore were inherently brittle: as human behavior changed, the models failed. “in physics you’re playing against God, and He doesn’t change His laws very often. In finance, you’re playing against God’s creatures.”

I just read a fascinating book review in the Wall Street Journal Physics Envy: Models Behaving Badly. The author of the book, Emanuel Derman (former head of Quantitative Analsis at Goldman Sachs) argues that the financial models involved human beings and therefore were inherently brittle: as human behavior changed, the models failed. “in physics you’re playing against God, and He doesn’t change His laws very often. In finance, you’re playing against God’s creatures.”

I’ll agree with Derman that whenever human beings are in the loop, data suffers. People change their minds based on information not available to the models.

I also agree that human behavioral modeling is not the same as physical modeling. We can use the latter to provide motivation and even mathematics for human behavioral modeling, but we should not take this too far. A simple example is this: purchase decisions sometimes depend not on the person’s propensity to purchase alone, but also on whether or not they had an argument that morning, or if they just watched a great movie. There is an emotional component that data cannot reflect. People therefore behave in ways that on the surface are contradictory, seemingly “random”, which is way response rates of 1% can be “good”.

More Read

Mixed-Effects Models in R with Quantum Forest
How “Big Data” Is Protecting the Enterprise Against Growing Social Risk
Getting Smarter About Water?
How to Overcome Data Visualisation Problems
Is Facebook Taking Big Data Analytics Too Far?

However, I bristle a bit at the the emphasis on the physics analogy. In closed systems, models can explain everything. But once one opens up the world, even physical models are imperfect because they often do not incorporate all the information available. For example, missile guidance is based on pure physics: move a surface on a wing and one can change the trajectory of the missile. There are equations of motion that describe exactly where the missile will go. There is no mystery here.

However, all operational missile guidances systems are “closed loop”; the guidance command sequence is not completely scheduled but is updated throughout the flight. Why? To compensate for unexpected effects of the guidance commands, often due to ballistic winds, thermal gradients, or other effects on the physical system. It is the closed-loop corrections that make missile guidance work. The exact same principal applies to your car’s cruise control, chasing down a fly ball in baseball, or even just walking down the street.

For a predictive model to be useful long-term, it needs updating to correct for changes in the population the models are applied to, whether the models be for customer acquisition, churn, fraud detection, or any model. The “closed-loop” typical in data mining is called “model updating” and is critical for long-term modeling success.

The question then becomes this: can the models be updated quickly enough to compensate for changes in the population? If a missile can only be updated at 10Hz (10x / sec.) but uncertainties effect the trajectory significantly in milliseconds, the closed-loop actions may be insufficient to compensate. If your predictive can only be updated monthly, but your customer behavior changes significantly on a weekly basis, your models will be behind perpetually. Measuring the effectiveness of model predictions is therefore critical in determining the frequency of model updating necessary in your organization.

To be fair, until I read the book I have no quibble with the arguments. The arguments here are based solely on the book review and some ideas they prompted in my mind. I’d welcome comments from anyone who has read the book already.

The book can be found on amazon here.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

sales and data analytics
How Data Analytics Improves Lead Management and Sales Results
Analytics Big Data Exclusive
ai in marketing
How AI and Smart Platforms Improve Email Marketing
Artificial Intelligence Exclusive Marketing
AI Document Verification for Legal Firms: Importance & Top Tools
AI Document Verification for Legal Firms: Importance & Top Tools
Artificial Intelligence Exclusive
AI supply chain
AI Tools Are Strengthening Global Supply Chains
Artificial Intelligence Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

predictive analytics marketplace
AnalyticsBig DataBusiness IntelligenceModelingPredictive Analytics

Predictive Analytics in the Marketplace: Insights from PAWCON

4 Min Read

Open Data App for the Paris Métro

3 Min Read

7 Questions Every Data Scientist Should Be Answering for Business

8 Min Read
Image
AnalyticsData ManagementModeling

How Machine Learning Could Result In Great Applications for Your Business

8 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?