Developing a method to quantitatively represent dynamic behavior of agents on longitudinal data. We propose a framework to discover and analyze: 1. Positioning among competing and collaborating agents (e.g. companies) 2. (Market) Diversification 3. Leaders/Followers 4. Response to Industry trends

1. Modeling Dynamics
from Longitudinal Data
Péter Molnár
https://www.linkedin.com/in/petermolnar/

2. What is this talk about?
Developing a method to quantitatively represent
dynamic behavior of agents
1. Positioning among competing and collaborating
agents (= companies)
2. (Market) Diversification
3. Leaders/Followers
4. Response to Industry trends
on longitudinal data…

3. Social Force Theory
͢

4. Gestalt psychologist, in the 1940s

5. How Competitive Forces Shape Strategy
Michael Porter
1. Suppliers (Bargaining Power)
2. Customers(Bargaining Power)
3. New Entrants (Threat)
4. Substitute Products of Services (Threat)
5. Industry (Trend)
6. Current Competitors (Positioning)
https://hbr.org/1979/03/how-competitive-forces-shape-strategy

6. Social Impact Theory Bibb Latané
Social impact is defined as any influence on individual feelings, thoughts or
behavior that is created from the real, implied or imagined presence or actions of
others.
• Strength (S) is a net of all individual factors that make a person influential.
• Immediacy (I) takes into account how recent the event occurred and whether or
not there were other intervening factors
• The number of sources (N) refers to the amount of sources of influence

7. Modeling Human Behavior:
How do people walk?

8. Social Forces
Pair-interactions
Repelling Potential
Attracting Potential
Distance R

9. Social Force Theory

10. Self-organized Flow Patterns

11. Dirk Helbing, Joachim Keltsch & Péter Molnár - Nature 388, 47-50 (3 July 1997)
Trail Formation

14. Definition of
Dimensions?
Meaning of
Positions?
Gartner MQ
Cloud Infrastructure
as a Service
June 2017

15. Gartner MQ
Cloud Infrastructure as a Service
June 2017
https://gigas.com/blog/wp-content/uploads/2017/11/2017-Magic-Quadrant-for-Cloud-Infrastructure-as-a-Service-Gigas-pag-51.pdf
Product or Service High
Overall Viability High
Sales Execution/Pricing Medium
Market Responsiveness/Record High
Marketing Execution Medium
Customer Experience Medium
Operations Medium
Market Understanding High
Marketing Strategy Medium
Sales Strategy Medium
Offering (Product) Strategy High
Business Model Medium
Vertical/Industry Strategy Low
Innovation High
Geographic Strategy Low
Completeness of Vision
Ability to Execute

16. Gartner MQ
Cloud Infrastructure as a Service
June 2017
201520142013
2016 2017

17. How to
determine the
Leader,
Challenger,
Niche Player, or
Visionary?
Where's the
dynamics?

18. Challengers Leaders
VisionariesNiche Players

19. Bargaining Power Threats
CompetitorsTrends
Porter's Forces

20. Vector Space Encoding

21. Dimensionality Reduction
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Quadratic Discriminant Analysis (QDA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)

22. Auto-Encoder

23. Word2Vec/Doc2Vec
Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
https://arxiv.org/abs/1301.3781
Distributed Representations of Sentences and Documents
Quoc V. Le, Tomas Mikolov
https://cs.stanford.edu/~quocle/paragraph_vector.pdf

24. Collaborative Filters/Factorization
• Alternating Least Squares (ALS)
• Singular Value Decomposition
• Non-negative matrix factorization (NMF/NNMF)
Almohsen, Khadija & Al-Jobori, H. (2015). Recommender systems in light of big data. 5. 1553-1563.

25. Dimensionality Reduction Auto Encoder
word2vec Matrix Factorization

26. Putting is all together …

27. T0
T1
T2
Fij
Vector-space
embedding of features
Trajectories and pairwise
distances Rij(T)
Computing
Fij(T) =
d Rij(T)
dT

28. Multi-agent Simulation
Fij

29. Péter Molnár
https://www.linkedin.com/in/petermolnar/
T0
T1
T2