2026 PAW: Decision-Making Beyond Facts and Law at the 10th ICC European Conference

Key Takeaways

• PAW reaches 288 partners, 40,000 registrations.
• Myside bias skews parties' success predictions.
• Overconfidence hampers realistic settlement offers.
• Diverse tribunals reduce confirmation bias.
• AI tools stress‑test arguments and cost allocations.

Pulse Analysis

Cognitive biases remain a hidden force in arbitration, shaping how counsel evaluate evidence and predict outcomes. The PAW conference highlighted myside bias, where lawyers favor information confirming their client’s position, and overconfidence, a meta‑belief in initial judgments that discourages reassessment. These distortions can inflate parties’ success expectations, reducing settlement likelihood. Mitigation tactics such as drafting arguments from the opponent’s perspective and leveraging AI to benchmark predictions are gaining traction, offering a data‑driven check on instinctual reasoning.

Diversity and inclusion emerged as strategic safeguards against collective blind spots within tribunals. Panels stressed that cultural, legal, and professional variety among arbitrators disrupts echo‑chamber dynamics, fostering more rigorous fact‑finding and reducing confirmation bias. Effective chairmanship—through pre‑hearing meetings and encouraging junior arbitrators to voice concerns—further promotes balanced deliberations. When dissenting opinions arise, they can clarify reasoning and, in some cases, steer the majority toward a more robust, unanimous award, enhancing the legitimacy of the decision.

Financial incentives and technology are reshaping arbitration practice. Fee models ranging from hourly billing to success‑based arrangements influence counsel behavior, while litigation funders prioritize recoverable value and enforceability, often steering parties toward settlement. AI platforms like Jus AI provide rapid case law analysis and scenario testing, enabling teams to stress‑test strategies and anticipate cost implications. Together, these developments point to a more transparent, data‑informed arbitration landscape where bias is acknowledged, diversity is institutionalized, and financial efficiency is optimized.