Applying Weighted Linear Combinations to determining motivation wave level.

1. Weighted Linear
Combination
Applied to Motivation Waves
Stewart MacGregor-Dennis

2. Abstract
• A weighted linear combination is a type of evaluation function used
in artificial intelligence, where an evaluation function is an equation
that returns a score for a particular state1. A state is just what the
world looks like right now (i.e. your immediate environment, current
level of social pressure to exercise, etc). Using a weighted linear
combination as an evaluation function is a step towards the
automation of anticipating and recognizing when someone is in a
motivation waves. Using this function and an appropriate data
set, we could search through a set of possible behavior design
interventions to find which one maximizes a person’s motivation
score. In practice, we would most likely want to balance this
motivation score against changes in their ability to act and trigger
levels too2.
1. Russell, Stuart J., and Peter Norvig. Artificial Intelligence: A Modern
Approach. Englewood Cliffs, NJ: Prentice Hall, 1995. Print.
2. Fogg, BJ. A Behavior Model for Persuasive Design. N.p., n.d. Web.
<http://www.bjfogg.com/fbm_files/page4_1.pdf>.

3. Variables
• Way of recognizing motivation waves: • Conducive environment
• Already a habit • Factor:
• Factor: • ENV = Conduciveness of environment
• TIME = Length of time since began habit for specific behavior
• Way of recognizing motivation waves:
• Authority / Friend Request • Way of recognizing motivation waves:
• Factor: • Prolonged inactivity
• SOC = measure of social pressure • Factor:
• Way of recognizing motivation waves: • INACT-TIME = Time inactive
• Survival from bad health • Way of recognizing motivation waves:
• Factor: • Extreme emotions
• HSURV = Current health profile score • Factor:
• Way of recognizing motivation waves: • EMO = Emotional score
• Survival from physical risks • Way of recognizing motivation waves:
• Factor: • Self-reflection/realization
• RSURV = Current risk profile score • Factor:
• Way of recognizing motivation waves: • REFLECT = Quality of self-
• Social norms reflection/realization
• Factor: • Way of recognizing motivation waves:
• STRNORM = Strength of associated • Imaginary judgement
social norms • Factor:
• Way of recognizing motivation waves: • IMAG-JUDGE = presence of imaginary
judgement
Ngo, David. "9 Ways to Recognize Motivation Waves." 9 Ways to
Recognize Motivation Waves. N.p., n.d. Web. 27 Oct. 2012.
<http://www.slideshare.net/dngo11/9-ways-to-recognize-motivation-
waves>.

4. Evaluation Function
• Motivation = 0.1 * TIME + 0.15 * SOC + 0.1 * HSURV +
0.125 * RSURV + 0.75 * STRNORM + 0.175 * ENV + 0.1 *
INACT-TIME + 0.025 * EMO + 0.1 * REFLECT + 0.05 *
IMAG-JUDGE

5. Next steps
• Write a program to implement the function.
• Start testing the function with different data sets.
• Apply learning/optimization to get a more accurate
weighting of the variables.