Game Theory: Why do US sports owners spend millions on politics? US sports team owners include some of the wealthiest people in the world, and many spend millions of dollars on US politics. But why? Ahead of the US midterm elections, Al Jazeera's Samantha Johnson follows the money to explore why sports owners spend millions on politics. Why did Africa boycott the 1966 World Cup? Why are World Cup tickets so expensive?

What does it cost an athlete to take a stand? From Muhammad Ali and Colin Kaepernick to Azeez Al-Shaair and Naomi Osaka, athletes have spoken out on issues they believe in, sometimes at great personal and professional cost. Al Jazeera's Samantha Johnson explores the price athletes pay for their activism. Why did Africa boycott the 1966 World Cup? Why are World Cup tickets so expensive?

Game Theory: What happens when you break a sporting boycott for money? Would you break one of sport's biggest boycotts for life-changing money? In the 1980s, some West Indies cricketers toured apartheid South Africa, sparking outrage. Al Jazeera's Samantha Johnson explores the money, morals and consequences behind the controversial tours. Why did Africa boycott the 1966 World Cup?

In a new series of interviews, we're meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) . This time, we meet Adeela Bashir to find out how she is contributing to the field of cybersecurity. Tell us a bit about your PhD - where are you studying, and what is the topic of your research? I am a PhD researcher at SCEDT, Teesside University in the UK, working at the intersection of Cybersecurity, Evolutionary Game Theory (EGT), and AI safety. My PhD asks a central question: How can we understand and mitigate cyber threats when the adversaries are adaptive?

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Self-evolution offers a scalable path to stronger reasoning: a pretrained language model improves itself with only minimal external supervision. Yet existing methods either depend on extensively curated or teacher-generated training data, or, when the generator runs unsupervised, reward it by a difficulty heuristic that need not improve the solver. We introduce INFUSER, an iterative co-training framework with two co-evolving roles: a Generator that drafts questions and reference golden answers from a pool of unstructured, automatically collected documents, and a Solver that improves by training on them. The solver is trained with standard correctness rewards against the generator-provided answers, while the generator is rewarded by an optimizer-aware influence score that measures whether each proposed question would actually improve the solver on the target distribution. Because this continuous, noisy influence score is poorly served by standard GRPO, we propose DuGRPO, a dual-normalized variant of GRPO, for generator training. Together, these turn the document pool into an adaptive curriculum that favors questions useful to the current solver, not just hard ones. On Qwen3-8B-Base, INFUSER outperforms strong self-evolution baselines with over 20% relative improvement on Olympiad and SuperGPQA benchmarks, and an 8B INFUSER co-evolving generator outperforms a frozen 32B thinking generator on math and coding. Ablations confirm each design choice is necessary, and two extensions, applying INFUSER to an instruction-finetuned anchor and augmenting it with rule-verifiable RLVR data, further demonstrate the flexibility and generalizability of the framework. Code is available at https://github.com/FFishy-git/INFUSER.

Game Theory: Why did Africa boycott the 1966 World Cup? Game Theory Why did Africa boycott the 1966 World Cup? A record 10 African teams are competing at the 2026 World Cup. But 60 years ago, not one African nation played in the 1966 World Cup. Al Jazeera's Samantha Johnson looks at the 1966 boycott that helped reshape the tournament for generations to come. Why are World Cup tickets so expensive?

Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data or rely on similar pre-trained models, the result is correlated predictions. In the context of personalized pricing, correlated algorithms can be viewed as a means to collude among competing firms, but whether or not this conduct is legal depends on the mechanisms of achieving collusion. We investigate the precise mechanisms through a formal game-theoretic model. Indeed, we find that (1) higher correlation diminishes consumer welfare and (2) as consumers become more price sensitive, firms are increasingly incentivized to compromise on the accuracy of their predictions in exchange for coordination. We demonstrate our theoretical results in a stylized empirical study where two firms compete using personalized pricing algorithms. Our results demonstrate a new mechanism for achieving collusion through correlation, which allows us to analyze its legal implications. Correlation through algorithms is a new frontier of anti-competitive behavior that is largely unconsidered by US antitrust law.

Accurate attribution for multiple platforms is critical for evaluating performancebased advertising. However, existing attribution methods rely heavily on the heuristic methods, e.g., Last-Click Mechanism (LCM) which always allocates the attribution to the platform with the latest report, lacking theoretical guarantees for attribution accuracy. In this work, we propose a novel theoretical model for the advertising attribution problem, in which we aim to design the optimal dominant strategy incentive compatible (DSIC) mechanisms and evaluate their performance. We first show that LCM is not DSIC and performs poorly in terms of accuracy and fairness. To address this limitation, we introduce the Peer-Validated Mechanism (PVM), a DSIC mechanism in which a platform's attribution depends solely on the reports of other platforms. We then examine the accuracy of PVM across both homogeneous and heterogeneous settings, and provide provable accuracy bounds for each case. Notably, we show that PVM is the optimal DSIC mechanism in the homogeneous setting. Finally, numerical experiments are conducted to show that PVM consistently outperforms LCM in terms of attribution accuracy and fairness.

Shapley values have emerged as a critical tool for explaining which features impact the decisions made by machine learning models. However, computing exact Shapley values is difficult, generally requiring an exponential (in the feature dimension) number of model evaluations. To address this, many model-agnostic randomized estimators have been developed, the most influential and widely used being the KernelSHAP method (Lundberg & Lee, 2017). While related estimators such as unbiased KernelSHAP (Covert & Lee, 2021) and LeverageSHAP (Musco & Witter, 2025) are known to satisfy theoretical guarantees, bounds for KernelSHAP have remained elusive. We describe a broad and unified framework that encompasses KernelSHAP and related estimators constructed using both with and without replacement sampling strategies.