Nobody needs that

Adaption of innovation
Dr. Sasha Göbbels
http://slides.technologyscout.net/dbks-en/

What's the deal?

WOW, THIS IS COOL!!11eleven

Nobody needs that.

I have no idea for what this is good for!

Foundation: Sociology

Sociology researches all aspects of societal coexistance of people in communities and societies.
Source: Internet

Everett Rogers

Diffusion of innovations, 1962

Henrik Vejlgaard

Anatomy of a trend, 2007

Marketing: Geoffrey Moore

Crossing the chasm

Classification of innovations

  • Timeline of innovations: continuously vs. disruptive
  • Levels of decisions: commercial vs. cultural
  • Expectations of players: visionary vs. pragmatic

Too bad: almost never "pure" forms!

Mechanism of adaption

Classical Approach

  • Players decide individually
  • Decision after weighing the advantages and disadvantages
  • cost-of-change

Disruptive innovations

Marketing: Geoffrey Moore ↝

  • Change communication at the gap:
    • Left/before (Visionary): Coolness and potential
    • Right/after (Pragmatic): Narrative about practical application
  • Define target markets
  • One segment as a springboard for the next

Mechanisms of adaption

Criticism of continuous theory

  • The independent decision-making process can never lead to an "s curve"
  • Result are "r curves"
  • Combination of two methods creates s curves:
    • individual cost-of-change/benefit considerations
    • "biased transmission"

What's all this for?

  • Technical innovations like wearables are disruptive
  • The behavior of some people may seem strange to observers. Why?
  • How can one understand how disruptive innovations conquer a population or not??
  • Can one use the understanding of the processes?

What's that to do with computer science?

  • Observe what's happening
  • Create models for the mechanisms at work
  • How to approve a model?
Simulate it! Don't leave the sociologists alone!

Simulation with cellular automata

Robert Axelrod, 1997

Robert Axelrod in 1997 developed a system, consisting of a net of cellular automata, that simulate a population. Each individual has a set of features, represented by a tupel or vector of numbers. Individuals take on features from their neighbors by interactions.

Axelrod Model

Algorithm

  1. Create a field of individuals (e.g. 50×50) and set features
  2. Select one cell randomly
  3. Select a random neighbor (N,S,E,W)
  4. Calculate similarity of participants, from this calculate a probability for interaction
  5. If likely: take on a randomly selected feature from the neighbor
  6. Back to step 2. as often as you like

Axelrod Model

Possible end states

  • Complete homogeneity, all individuals are equal
  • Few large areas, called cultural domains
  • Complete chaos

Axelrod Modell

Size: 30×30, No. of features: 3, max. value: 6, Threshold: 0,1, 100.000 Iterations. Built with p5.js

Axelrod Model

Variations

  • Rejection if too few similarities:
    a feature shares by both individuals is changed for one of them
  • Additional influence:
    • Mass media
    • Social networks
    • biased transmission
    • long-range interactions (Small World Model, Jon Kleinberg)

References 1

  • Everett Rogers, "Diffusion of innovations", 1962-2003
  • Henrik Vejlgaard, "Anatomy of a trend", 2007
  • Martin Raymond, "The Trend Forecaster's Handbook", 2010
  • Robert Axelrod, "The Dissemination of Culture", J. Confl. Res., Vol. 41 Nr. 2, 1997
  • Joseph Henrich, "Cultural Transmission and the Diffusion of Innovations", Am. Anthropologist 2001, 103(4) 992-1013

References 2