š§ AI Autonomous Goal-Setting
As AI systems gain increasing autonomy, they must navigate environments where objectives arenāt fixed, inputs are noisy, and long-term consequences are hard to model. We investigate how goal-setting processes evolve within autonomous agents, using analogies from biological adaptation and evolutionary psychology. Our models explore how AI systems balance exploration vs. exploitation, update internal utility functions, and develop persistent behaviors in complex digital landscapes.
Can autonomous AIs āevolveā misaligned goals? What selective pressures shape the emergence of long-term strategy in self-directed systems?
šĀ Evolution of AI Apps in Digital Ecosystems
Digital tools compete for user attention, compute resources, and integration opportunitiesāmuch like organisms compete for food, mates, and habitat. We model AI apps as evolving entities in digital ecosystems, subject to niche competition, extinction risk, and innovation shocks. This work draws on ecological modeling, evolutionary theory, and complexity science to forecast survivability and saturation in real-world software environments.
What makes an AI app adaptive over time? What traits predict resilience vs. obsolescence in shifting technical ecosystems?
āļøĀ AI Ethics and Oversight
We examine the evolutionary forces shaping not just AI systems, but the governance structures that attempt to control them. Ethical principles, audit frameworks, and regulatory efforts themselves behave like adaptive organismsāemerging, spreading, or collapsing in response to social, political, and economic selection pressures. Our models simulate how oversight systems evolve and fail under asymmetric information, power imbalances, and global digital inequality.
Can ethical frameworks for AI survive in hostile economic environments? How can we model the evolution of norms, not just code?
š¤Ā HumanāAI Interaction
Beyond interface design, we treat humanāAI interaction as a dynamic co-evolutionary system: humans shape the behavior of AIs, and AIs reshape the cognitive ecology of their users. We explore the feedback loops between human goals, trust signals, behavioral nudges, and AI responsiveness. Our simulations help clarify the tipping points at which assistance turns to dependenceāand how to design for long-term alignment and agency.
How do AI systems and human users evolve together? What hidden traits make systems addictive, trusted, or fragile in the long run?