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              <text>This project extends the framework of Leung and Turrini (AAMAS 2024) by introducing behavioural diversity into populations of reinforcement learning agents playing repeated Prisoner's Dilemma games with partner selection. Agents differ in their learning rate and exploration-exploitation balance, forming “curious” and “conservative” types. Simulations show that heterogeneity sustains long-term cooperation. These insights contribute to understanding how diversity influences collective learning dynamics in multi-agent systems.</text>
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              <text>This project analyses 20 consulting job advertisements, coded using signalling theory (Connelly et al., 2011) into three core themes: Career Progression (CP), Growth Opportunities (GO), and Work-Life Balance (WLB). The study examines how firm size influences employer branding narratives and the messages received by applicants.</text>
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