Our labels: data scientist vs statisticians (or OR)

A perennial discussion of identities in the world of analytics is making the rounds on the blogs of statisticians. Or wait a second, what should we call them?
Data scientist is just a sexed up word for statistician

Data Scientists, Statisticians, Applied Mathematicians, Operational Researchers...jus to name a few, are the labels one might apply to themselves in the field of analytics. How shall we label ourselves? I can't agree more with Nate Silver,

"Just do good work and call yourself whatever you want."

What qualifies as a Simulation Model?

A theme that has been running through my career since my Master's project has been the question of measuring complexity in modelling and simulation. When can one proclaim to have built a simulation model and when is one glorifying simple analysis?

In the Operations Research ecosystem the tendency is certainly to inflate. Salesmen, curriculum vitae authors, recruiters and consultancies across the spectrum are all motivated to embellish the work that they do and work that is done. Like any scientific individual I seek to slice through the static, inform myself as to who is doing extraordinary work, and to build myself a framework from which I can safely criticize the inflations of others.

I have been working on a set of rules for separating "models" into models, calculations and simulations. I feel like there is a gaping opportunity here for contribution from complexity, chaos, and other disciplines in Computer Science and Mathematics, but here's what I've put together thus far:

Simulations are models, but not all models are simulations. Calculations are not models.

Models

1. A model is a simplified representation of a system.
2. All models are wrong, but some models are useful

Calculations

1. The result of a calculation can be expressed in a single equation using relatively basic mathematical notation.
2. Where calculations contain an time element, values at different times can be determined in any order without referring to previous values.

Simulations

1. A simulation is a calculation in which one parameter is the simulation clock that increments regularly or irregularly.
2. The outcome of a simulation could not have been determined without the use of the clock.
3. While an initial state is typically defined, an intermediate state at a given time should be difficult or impossible to determine without having run the simulation to that point.
4. Almost any model that involves repeated samples of random numbers should be classified as a simulation.

Consider the following progression of "models" that output an expected total savings:

1. Inputs: Expected total savings.
2. Inputs: Annual savings by year, time-frame of analysis.
3. Inputs: Annual savings per truck per year, number of trucks by year, time-frame of analysis.
4. Inputs: Annual savings per truck per year, current number of customers, number of trucks per customer, annual increase in customers, time-frame of analysis
5. Inputs: Annual savings per truck per year, current number of customers by geographical location, annual increase in customers by geographical location, routing algorithm to determine necessary trucks, time-frame of analysis.
6. Inputs: Annual savings per truck per year, current number of customers by geographical location, distribution of possible growth in customers by geographical location, routing algorithm to determine necessary trucks, time-frame of analysis.

As you can see, complexity builds and eventually passes a threshold where we would accept it as a model. "Model" 4 is still little more than a back of the envelope calculation, but Model 5 takes a quantum leap in complexity with the introduction of the algorithm. Model 5 however I would still not classify s a simulation, because any year could be calculated without having calculated the others. Finally Model 6 introduces a stochastic variable (randomness) that compounds from one year to another and brings us to a proper simulation.

I've seen calculations masquerading as simulations models at a Fortune 500 company both internally and externally. While the result is the same: outcomes determined from data where validity is asserted by the author, I know that Operational Research practitioners reading this will appreciate my desire to classify. At the very least it will help us separate what the MBAs do with spreadsheets from our own work.

I welcome input from others on this topic, as I am only just developing my own theories.

Psychotherapy and Operational Research / Change Management

Happy New Year to the ThinkOR readers and the Operational Research community.

What better way to celebrate the new year than learning something new!

1. "Although there are many details about our patients that we cannot know, nonetheless, our task is to delimit a system of observation in which we can trace the essential causal chains, and find accessible points, or handles, where interventions can be made."

2. "...It is perhaps clear... that the choice of a system is not only dependent upon the nature of reality, but also upon the means we have to investigate it and the purpose of the inquiry. The larger the system we choose, the safer we can be in assuming that it will include the relevant causal relationships. However, such a system may not be manageable and therefore of no help at all."

Upon first glance, these would look like quotes from an Operational Research book. However, they are in fact quotes from a book titled Integrated Psychotherapy, published in 1979 by the wonderful family friends, Doctors Ferdinand Knobloch and Jirina Knobloch, who are renowned Psychiatry Professors specialising in psychotherapy. I want to share with you the similarity of a psychotherapist's task and an OR practitioner's.

Never would I have thought that there'd be anything in common between Operational Research and Psychotherapy, a branch of Psychiatry, treating patients with mental health problems through communication and contact, without medication. Wikipedia's definition of Integrated Psychotherapy is:

Integrative psychotherapy may involve the fusion of different schools of psychotherapy. The word 'integrative' in Integrative psychotherapy may also refer to integrating the personality and making it cohesive, and to the bringing together of the "affective, cognitive, behavioral, and physiological systems within a person".

The first quote from the Integrated Psychotherapy about a psychotherapist's task made me think of my work immediately. I am currently a project manager at a children's hospital in London working on process improvement and transformation projects. When we go about solving systematic problems within a process to improve it, it is impossible that we understand all details of such a process. Our goal is, as exactly Dr Knobloch's describe, to find out enough information to diagnose the problem, understand why the problem exists ("trace the essential causal chains"); then we need to identify the levers to improve upon it, to successfully apply any change management ("find accessible points, or handles, where interventions can be made").

The second quote about the choice of a system rings rather true for any simulation projects. The perfect system is the real world itself, but it would be rather impossible to simulate it.

Learn about Operations Research in a video

This is a great introduction video to operations research - created by INFORMS.

What is Operations Research

Quoting from Wikipedia, Operations Research is:

an interdisciplinary branch of applied mathematics which uses methods like mathematical modeling, statistics, and algorithms to arrive at optimal or good decisions in complex problems which are concerned with optimizing the maxima (profit, faster assembly line, greater crop yield, higher bandwidth, etc) or minima (cost loss, lowering of risk, etc) of some objective function. The eventual intention behind using operations research is to elicit a best possible solution to a problem mathematically, which improves or optimizes the performance of the system.

Another definition of OR, given at one of the Plenaries at the last INFORMS Meeting (Seattle 2007).

A path was defined to unify Industrial Engineering, Operations Reaearch, Operations Management, etc as "Operations Engineering". The preferred was "Operations Science and Engineering" but I like the idea of having a distinct name for what is research from what is practice and application.

From ScienceOfBetter.org:

In a nutshell, operations research (O.R.) is the discipline of applying advanced analytical methods to help make better decisions.

By using techniques such as mathematical modeling to analyze complex situations, operations research gives executives the power to make more effective decisions and build more productive systems based on:

• More complete data
  
• Consideration of all available options
  
• Careful predictions of outcomes and estimates of risk
  
• The latest decision tools and techniques
  

To define our profession is one of the most difficult things - so I've found.

What is your definition? How do you think we should be "marketed"? Post your comment.