The idea of this course is to approach complex systems in a practical way, that allows active learning.
Complex systems are networks made of a number of components that interact with each other, typically in a nonlinear fashion. Complex systems may arise and evolve through self-organization, such that they are neither completely regular nor completely random, permitting the development of emergent behavior at macroscopic scales. H. Sayama
Systems thinking (ST) is a holistic approach to analysis that focuses on the system as unity, taking into account a system’s parts, their interconnections, and the whole system’s evolution over time and over space. System thinking allows future leaders to see the big picture and understand and influence the consequences of their decisions. The core idea is to view the problem as a part of big interconnected system, so one can understand the system only after understanding its elements and their interconnections. ST can be applicable to every complex system, e.g. project management, economy, politics, society, and education, among many
Complexity science (CS) proposes the extension of ST paradigm, allowing to deal with rather complex than complicated systems. CS view on the complex system is to admit that complexity arises between deterministic and chaotic behavior and a full understanding of system evolution requires multiple models and simulations.
1. Linear and Nonlinear dynamics.
a. Concepts. ODEs, stable points, equilibrium, iterative maps, chaos and bifurcations, attractors.
b. Models. Growth models, DLA model, Lotka-Volterra model, SIR model.
2. Spatial simulation and patterns.
a. Concepts. Self-organization, cellular automata, agent-based models, artificial societies.
b. Models. Segregation, flocking, simple economy, sugarscape model.
3. Networks and collective behavior.
a. Concepts. Small world networks, networks laws, scale-free networks, main attributes of network, networks evolution, SNA, tipping points.
b. Models. Waltz-Strogatz network, Barabasi model, random vs deterministic chaos model, epidemy based models on networks, information spreading in social
4. Game theory.
a. Concepts. Strategic and extensive forms, Nash equilibrium, game solution, evolutionary and repeated games, learning in games.
b. Models. Iterative prisoner’s dilemma (PD), spatial PD, Axelrod tournaments, El-Farol bar problem, reinforcement learning, multi-armed bandits.
Навчальний семестр: 5
Кількість кредитів: 6 ECTS
Освітня програма: Комп’ютерні науки,
ІТ та Бізнес-аналітика