Workshop 4 explores how MAIDs can inform cadCAD models, emphasizing a standardized structure for policy functions and state update functions, a process that offers a template for encapsulating agent logic, enabling greater abstraction and testability. In this way, MAIDs could allow non-coders to participate actively in creating the cadCAD model structure or aid translation by AI.
PART I: Introduction & MAIDs Review
00:00:00 Introduction and Greetings
00:00:00 Session Overview and Background
00:00:48 Multi-Agent Influence Diagrams (MAIDs)
00:03:46 Personal Background and Research Approach
00:05:34 Undergraduate Research and Team Building
00:07:14 MAIDs in Mathematical Pedagogy
PART II: MAIDS Informed CAD CAD Frameworks & Code Structures
00:10:25 Modeling Frameworks & CAD CAD
00:16:37 What MAID representations enable
00:18:45 CAD CAD Modeling
00:20:09 Value of Qualitative Representations & Graph Structure of MAIDs
00:21:43 Translating MAIDs to cadCAD Model Structure
00:22:50 cadCAD Partial State Update Blocks
00:24:32 Modeling Process
PART III: MAIDS Informed Agent-Based Approach to cadCAD
00:28:36 Coding Framework for cadCAD
00:31:20 Connection to MSML
00:33:42 Agent Decisions
00:35:25 Agent Strategy
00:36:10 Agent Utility Functions
00:37:14 Possibilities of Agent Structure
00:42:06 Discussion: Q&A
00:44:26 cadCAD World Model
00:46:46 Board Game Analogy: MAIDS & cadCAD
00:49:24 Complexity in State Space & Rule Sets
00:51:54 Decision Rules & Strategy Space
00:54:52 Agent-Based Modeling & Network Policies
00:58:13 Agent Modeling as a Philosophy
00:59:21 Space & Time, Agents & Abstract Patterns
01:01:48 GDS & Mapping the Admissible Action Space
01:02:53 GDS for Non-Trivial Choice
01:06:05 Combining Rules into Strategies
01:07:13 Agents as Parameters
01:08:40 Parameters to Simulations to Experiments
01:09:16 Designing Experiments
01:09:56 Philosophical Questions on Passive Agents
PART IV: Applied & Future Directions
01:14:43 Practical Engineering Application
01:16:14 The Search for Nash Equilibrium
01:21:55 Learning Rules & Second Order Systems
01:23:21 Dynamic Games & Strategy Updates
01:31:17 Final Thoughts & Future Directions
Access Resources via blog.block.science/complex-systems-modeling-maids-gds-cadcad/#workshop-4-the-modeling-process-future-directions
Led by Octopus, a Blockscience Research Engineer, this workshop series is designed to be broadly accessible to researchers, engineers, and teams comfortable with scientific computing in Python and familiar with or interested in modeling dynamic games. Required mathematical literacy includes linear algebra, dynamical systems, and event-driven systems. Knowledge of real analysis, optimization, the existence and uniqueness of equilibria, multi-agent systems, and potential games will aid your understanding.
⚠️Caveat: Throughout the series, you may notice that we touch on relevant technical concepts and alternative approaches only briefly, choosing to focus on the application of MAIDS to inform modular model structures and optimization problems. For example, in the third workshop, we avoid established algorithms for finding Nash Equilibria. Instead, we focus on finding Nash Equilibria via optimization processes, treating this as a black-box optimization problem without a special structure.
Other Videos in this series
ua-cam.com/video/GK5oRDTa72o/v-deo.html
ua-cam.com/video/o8oCgaN2tpQ/v-deo.html
ua-cam.com/video/aG8UzKtVuiQ/v-deo.html
BlockScience® is a complex systems engineering, R&D, and analytics firm. By integrating cutting-edge research, applied mathematics, and computational science, we analyze and design safe and resilient socio-technical systems. With deep expertise in Market Design, Distributed Systems, and AI, we provide engineering, design, and analytics services to a wide range of clients, including for-profit, non-profit, academic, and government organizations.
Our R&D occurs in iterative cycles between open-source research and software development, and application of the research and tools to client projects. Our client work includes design and post-launch monitoring and maintenance of economic and governance mechanisms based on research, simulation, and analysis. With our unique blend of engineering, data science, and social science expertise, the BlockScience team aims to diagnose and solve today’s most challenging frontier socio-technical problems.
► Discover More
block.science/
blog.block.science
x.com/block_science
linkedin.com/company/blockscience/