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Navigating the Future: The Role of Democratic Governance to Solve Global Challenges and AI Risks

In a world brimming with complexities and uncertainties, we find ourselves at a crossroads. We face a myriad of pressing global challenges — from climate change9 and social inequality8 to the ethical dilemmas posed by the rapid advancement of artificial intelligence (AI)4.

Imagine a future where AI surpasses individual human intelligence across many domains3. How do we safely integrate this expertise into existing governance structures? At present, AI algorithms, mostly devoid of ethical considerations, perpetuate biases5 and render decisions that lack transparency or foresight2. How can we harness this burgeoning power effectively while mitigating its risks?

If you were forced to give a single solution to all the world's most pressing problems, what would it be? Universal education? Innovation and Technology? Or even AI?

The Imperative of Collective Intelligence

I posit that "enhancing governance" is the linchpin around which all solutions revolve. In other words, we must elevate our collective intelligence6 above all else to safely and effectively navigate our future.

In an era increasingly defined by the advent of general AI, we have more reason than ever to focus on democratizing governance and aligning AI development symbiotically within it7. Because at the heart of the AI dilemma lies the need for ethical decision-making and strategic planning — a terrain where AI's weaknesses are most pronounced. The principles of democratic governance offer a sturdy foundation, providing the checks and balances necessary to guide AI development in line with human values and long-term welfare.

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However, the importance of democratic decision-making transcends the realm of AI. It extends to our collective response to all global challenges, ensuring policies are inclusive, coherent, and accountable. Democratic governance fosters economic stability, social justice, and environmental stewardship — essential ingredients for navigating the complexities of the 21st century.

Using Multi-Agent-Based Simulations

To embark on improving governance, we must first delve into research. Traditionally, collective decision-making research has focused on evaluating methods against reasonable assumptions (like Pareto optimality, Condorcet consistency, non-dictatorship, etc.), demonstrating mathematically that no method will ever satisfy all criteria1. But the complexity of the problem is even greater when we consider collective decision-making in real-world societies. Real-world democratic governance goes far beyond merely aligning individual interests — it involves intricate path dependencies from past decisions, disinformation and lack of participation, to name just the most obvious.

Addressing these challenges requires a novel approach. This research aims to pioneer a new path by incorporating these complexities through multi-agent-based simulations.

While the model and research inquiries within the proposed master thesis can only represent this approach in its infancy, the potential of multi-agent-based modeling to eventually encapsulate all essential facets of real-world democratic governance can hardly be overstated10. To the best of our knowledge, this approach has not been systematically applied to researching social choice or collective decision-making. It stands poised to boost what may be the most underestimated cornerstone of human society: our collective intelligence.

References

  1. Felix Brandt, Vincent Conitzer, Ulle Endriss, Jérôme Lang, and Ariel D. Procaccia, editors. Handbook of Computational Social Choice. Cambridge University Press, 2016 ↩

  2. Jana Fehr, Brian Citro, Rohit Malpani, Christoph Lippert, and Vince I Madai. A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare. Frontiers in Digital Health, 6:1267290, 2024. ↩

  3. Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, and Owain Evans. Viewpoint: When will AI exceed human performance? evidence from AI experts. J. Artif. Intell. Res., 62:729–754, 2018. ↩

  4. Benjamin Hilton. Preventing an AI-related catastrophe: AI might bring huge benefits — if we avoid the risks. ↩

  5. Susan Leavy, Barry O’Sullivan, and Eugenia Siapera. Data, power and bias in artificial intelligence. CoRR, abs/2008.07341, 2020. ↩

  6. Jan Marco Leimeister. Collective intelligence. Business & Information Systems Engineering, 2:245–248, 2010. ↩

  7. Thomas W Malone. Superminds: The surprising power of people and computers thinking together. Little, Brown Spark, 2018. ↩

  8. Thomas Piketty. Das Kapital im 21. Jahrhundert. CH Beck, 2014. ↩

  9. Hans-Otto Pörtner, Debra C Roberts, H Adams, C Adler, P Aldunce, E Ali, R Ara Begum, R Betts, R Bezner Kerr, R Biesbroek, et al. Climate change 2022: Impacts, adaptation and vulnerability. IPCC Sixth Assessment Report, 2022. ↩

  10. Robert L Axtell and J Doyne Farmer. Agent-based modeling in economics and finance: Past, present, and future. In: Journal of Economic Literature (2022), pp. 1–10 ↩