Digital twins are a virtual replica of a physical asset, system, or process. This digital counterpart is continuously updated with real-time data to mirror the real-world entity it represents. With AI and machine learning algorithms, these digital twins can simulate, predict, and optimize performance.

ABM helps us understand the complex world of fisheries by modeling each fish or entity as its own agent with unique traits and behaviors. By doing this, we can simulate their interactions and see how they lead to larger patterns and changes, like how ocean temperature affects migration patterns in the Arctic.

Our agent-based model, enhances fishery biomass and biodiversity by precisely simulating ecosystems and supporting precision fisheries management. This approach ensures sustainable fishing practices that balance ecological health and economic viability.

By generating counterfactual (what if) scenarios, our modeling allows policymakers to explore alternative futures and understand the implications of various decisions before they're made. This is crucial for effective risk management in business and innovative thinking in policy formulation.