🔖 Confessions of the Pricing Man: How Price Affects Everything by Hermann Simon

Confessions of the Pricing Man: How Price Affects Everything by Hermann Simon (Springer, 2015)
The world’s foremost expert on pricing strategy shows how this mysterious process works and how to maximize value through pricing to company and customer.

In all walks of life, we constantly make decisions about whether something is worth our money or our time, or try to convince others to part with their money or their time. Price is the place where value and money meet. From the global release of the latest electronic gadget to the bewildering gyrations of oil futures to markdowns at the bargain store, price is the most powerful and pervasive economic force in our day-to-day lives and one of the least understood.

The recipe for successful pricing often sounds like an exotic cocktail, with equal parts psychology, economics, strategy, tools and incentives stirred up together, usually with just enough math to sour the taste. That leads managers to water down the drink with hunches and rules of thumb, or leave out the parts with which they don’t feel comfortable. While this makes for a sweeter drink, it often lacks the punch to have an impact on the customer or on the business.

It doesn’t have to be that way, though, as Hermann Simon illustrates through dozens of stories collected over four decades in the trenches and behind the scenes. A world-renowned speaker on pricing and a trusted advisor to Fortune 500 executives, Simon’s lifelong journey has taken him from rural farmers’ markets, to a distinguished academic career, to a long second career as an entrepreneur and management consultant to companies large and small throughout the world. Along the way, he has learned from Nobel Prize winners and leading management gurus, and helped countless managers and executives use pricing as a way to create new markets, grow their businesses and gain a sustained competitive advantage. He also learned some tough personal lessons about value, how people perceive it, and how people profit from it.

In this engaging and practical narrative, Simon leaves nothing out of the pricing cocktail, but still makes it go down smoothly and leaves you wanting to learn more and do more―as a consumer or as a business person. You will never look at pricing the same way again.

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🔖 Linear Operator Theory in Engineering and Science

Linear Operator Theory in Engineering and Science by Arch W. Naylor and George R. Sell (Springer, 2nd printing 2000 edition (October 4, 2013))
A unique introduction to the theory of linear operators on Hilbert space. The author presents the basic facts of functional analysis in a form suitable for engineers, scientists, and applied mathematicians. Although the Definition-Theorem-Proof format of mathematics is used, careful attention is given to motivation of the material covered and many illustrative examples are presented.

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5 business books you should read this year | World Economic Forum

5 business books you should read this year by Verne Harnish (World Economic Forum)
Fortune round-up 5 business books to learn from.

Continue reading “5 business books you should read this year | World Economic Forum”

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Checkin UCLA Store (Ackerman Union)

UCLA Store (Ackerman Union)

This looks like fun: Signals and Boundaries: Building Blocks for Complex Adaptive Systems by John H. Holland

Ackerman Union, Los Angeles, California, United States of America

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🔖 Want to read: From Bacteria to Bach and Back: The Evolution of Minds by Daniel C. Dennett

From Bacteria to Bach and Back: The Evolution of Minds by Daniel C. Dennett (W. W. Norton & Company; 1 edition, 496 pages (February 7, 2017))
One of America’s foremost philosophers offers a major new account of the origins of the conscious mind.

How did we come to have minds?

For centuries, this question has intrigued psychologists, physicists, poets, and philosophers, who have wondered how the human mind developed its unrivaled ability to create, imagine, and explain. Disciples of Darwin have long aspired to explain how consciousness, language, and culture could have appeared through natural selection, blazing promising trails that tend, however, to end in confusion and controversy. Even though our understanding of the inner workings of proteins, neurons, and DNA is deeper than ever before, the matter of how our minds came to be has largely remained a mystery.

That is now changing, says Daniel C. Dennett. In From Bacteria to Bach and Back, his most comprehensive exploration of evolutionary thinking yet, he builds on ideas from computer science and biology to show how a comprehending mind could in fact have arisen from a mindless process of natural selection. Part philosophical whodunit, part bold scientific conjecture, this landmark work enlarges themes that have sustained Dennett’s legendary career at the forefront of philosophical thought.

In his inimitable style―laced with wit and arresting thought experiments―Dennett explains that a crucial shift occurred when humans developed the ability to share memes, or ways of doing things not based in genetic instinct. Language, itself composed of memes, turbocharged this interplay. Competition among memes―a form of natural selection―produced thinking tools so well-designed that they gave us the power to design our own memes. The result, a mind that not only perceives and controls but can create and comprehend, was thus largely shaped by the process of cultural evolution.

An agenda-setting book for a new generation of philosophers, scientists, and thinkers, From Bacteria to Bach and Back will delight and entertain anyone eager to make sense of how the mind works and how it came about.

4 color, 18 black-and-white illustrations

🔖 Want to read: From Bacteria to Bach and Back: The Evolution of Minds by Daniel C. Dennett

 

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🔖 Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott (Portfolio, May 10, 2016)
The first generation of the digital revolution brought us the Internet of information. The second genera­tion—powered by blockchain technology—is bringing us the Internet of value: a new, distributed platform that can help us reshape the world of business and transform the old order of human affairs for the better.

Blockchain is the ingeniously simple, revolution­ary protocol that allows transactions to be simul­taneously anonymous and secure by maintaining a tamperproof public ledger of value. Though it’s the technology that drives bitcoin and other digital cur­rencies, the underlying framework has the potential to go far beyond these and record virtually everything of value to humankind, from birth and death certifi­cates to insurance claims and even votes.

Perhaps not necessarily this particular book which appears to be on the overview side, but sometime this year I’d like to delve more deeply into the concept of blockchain and the tech behind it.

Anyone have recommendations of books they liked?

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🔖 Advanced Data Analysis from an Elementary Point of View by Cosma Rohilla Shalizi

Advanced Data Analysis from an Elementary Point of View by Cosma Rohilla Shalizi (stat.cmu.edu)

Advanced Data Analysis from an Elementary Point of View
by Cosma Rohilla Shalizi

This is a draft textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes for 36-402 at Carnegie Mellon University.

By making this draft generally available, I am not promising to provide any assistance or even clarification whatsoever. Comments are, however, welcome.

The book is under contract to Cambridge University Press; it should be turned over to the press before the end of 2015. A copy of the next-to-final version will remain freely accessible here permanently.

Complete draft in PDF

Table of contents:

    I. Regression and Its Generalizations

  1. Regression Basics
  2. The Truth about Linear Regression
  3. Model Evaluation
  4. Smoothing in Regression
  5. Simulation
  6. The Bootstrap
  7. Weighting and Variance
  8. Splines
  9. Additive Models
  10. Testing Regression Specifications
  11. Logistic Regression
  12. Generalized Linear Models and Generalized Additive Models
  13. Classification and Regression Trees
    II. Distributions and Latent Structure
  14. Density Estimation
  15. Relative Distributions and Smooth Tests of Goodness-of-Fit
  16. Principal Components Analysis
  17. Factor Models
  18. Nonlinear Dimensionality Reduction
  19. Mixture Models
  20. Graphical Models
    III. Dependent Data
  21. Time Series
  22. Spatial and Network Data
  23. Simulation-Based Inference
    IV. Causal Inference
  24. Graphical Causal Models
  25. Identifying Causal Effects
  26. Causal Inference from Experiments
  27. Estimating Causal Effects
  28. Discovering Causal StructureAppendices
    • Data-Analysis Problem Sets
    • Reminders from Linear Algebra
    • Big O and Little o Notation
    • Taylor Expansions
    • Multivariate Distributions
    • Algebra with Expectations and Variances
    • Propagation of Error, and Standard Errors for Derived Quantities
    • Optimization
    • chi-squared and the Likelihood Ratio Test
    • Proof of the Gauss-Markov Theorem
    • Rudimentary Graph Theory
    • Information Theory
    • Hypothesis Testing
    • Writing R Functions
    • Random Variable Generation

Planned changes:

  • Unified treatment of information-theoretic topics (relative entropy / Kullback-Leibler divergence, entropy, mutual information and independence, hypothesis-testing interpretations) in an appendix, with references from chapters on density estimation, on EM, and on independence testing
  • More detailed treatment of calibration and calibration-checking (part II)
  • Missing data and imputation (part II)
  • Move d-separation material from “causal models” chapter to graphical models chapter as no specifically causal content (parts II and IV)?
  • Expand treatment of partial identification for causal inference, including partial identification of effects by looking at all data-compatible DAGs (part IV)
  • Figure out how to cut at least 50 pages
  • Make sure notation is consistent throughout: insist that vectors are always matrices, or use more geometric notation?
  • Move simulation to an appendix
  • Move variance/weights chapter to right before logistic regression
  • Move some appendices online (i.e., after references)?

(Text last updated 30 March 2016; this page last updated 6 November 2015)

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