The £5.30 orange juice that tells the story of why supermarket prices are sky high There has been more than a bitter twang in the glasses at British breakfast tables. Only five years ago, a typical supermarket own-label carton of orange juice could be bought for 76p for 1 litre. One colleague was outraged to be sent a bill for £9 for a glass of hangover-busting orange juice and lemonade at an unassuming little restaurant in Kent. Asked why so much, she was told that the orange juice - albeit freshly squeezed - accounted for £5.30 of the price. Yet as costs have surged, the taste is changing too, with certain manufacturers substituting oranges for mandarins to cut costs.

I'm in the midst of putting together a buying guide of indoor vertical gardening systems, and the Gardyn--the 30-plant Home 4.0, to be exact--was the first tester to arrive at my house. I had it unboxed and set up within a couple of hours, lights on and water pump running. Sure enough, within a couple of weeks, all of Gardyn's proprietary seed-filled yCubes had sprouted, and a couple of weeks after that, I was harvesting bowlfuls of herbs and salad greens. Even though from setup to harvest the Gardyn required the use of about five brain cells, I was quite pleased with myself, despite having long ago given up gardening outdoors due to deer, rabbits, and my own incompetence with anything other than starts from the big-box store. What I failed to understand, but would come to grasp with subsequent systems, was that indoor hydroponic gardening is just as hard in some ways as outdoor gardening.

Social norms are standards of behaviour common in a society. However, when agents make decisions without considering how others are impacted, norms can emerge that lead to the subjugation of certain agents. We present RAWL-E, a method to create ethical norm-learning agents. RAWL-E agents operationalise maximin, a fairness principle from Rawlsian ethics, in their decision-making processes to promote ethical norms by balancing societal well-being with individual goals. We evaluate RAWL-E agents in simulated harvesting scenarios. We find that norms emerging in RAWL-E agent societies enhance social welfare, fairness, and robustness, and yield higher minimum experience compared to those that emerge in agent societies that do not implement Rawlsian ethics.

Generative agents, which implement behaviors using a large language model (LLM) to interpret and evaluate an environment, has demonstrated the capacity to solve complex tasks across many social and technological domains. However, when these agents interact with other agents and humans in presence of social structures such as existing norms, fostering cooperation between them emerges as a fundamental challenge. In this paper, we develop the framework of a Normative Module: an architecture for generative agents designed to enhance cooperation by enabling agents to recognize and adapt to the normative infrastructure of a given environment, in the form of institutions that define acceptable behaviors within a group of agents. We focus on the equilibrium selection aspect of the cooperation problem and inform our agent design based on the existence of classification institutions that implement correlated equilibrium to provide effective resolution of the equilibrium selection problem. Specifically, the normative module enables agents to learn through peer interactions which of multiple candidate institutions in the environment, does a group treat as authoritative. By enabling normative competence in this sense, agents gain ability to coordinate their sanctioning behaviour; coordinated sanctioning behaviour in turn shapes primary behaviour within a social environment, leading to higher average welfare We design a new environment that supports institutions and evaluate the proposed framework based on two key criteria derived from agent interactions with peers and institutions: (i) the agent's ability to disregard non-authoritative institutions and (ii) the agent's ability to identify authoritative institutions among several options. Crucially, we show that these capabilities allow the agent to achieve more stable cooperative outcomes compared to baseline agents without the normative module, paving the way for future research in a new avenue of designing environments and agents that account for normative infrastructure.

Launched as if from a trebuchet at the end of April, Manor Lords is the latest in a string of explosively successful video games that have been released this year. Indeed, the rise of this unassuming-looking city-builder is arguably more impressive than the enormous launch of Helldivers 2, or the breakout Poker phenomenon Balatro. Developed largely by one person and releasing in an incomplete state, Manor Lords shifted a million copies in its first 24 hours on sale. The scale of Manor Lords' success is remarkable, but contrary to appearances, it hasn't emerged from nowhere. Momentum around the game has been building for years, part of a broader surge in popularity for city-building games in general.

Recent advances in multi-agent systems (MAS) have shown that incorporating peer incentivization (PI) mechanisms vastly improves cooperation. Especially in social dilemmas, communication between the agents helps to overcome sub-optimal Nash equilibria. However, incentivization tokens need to be carefully selected. Furthermore, real-world applications might yield increased privacy requirements and limited exchange. Therefore, we extend the PI protocol for mutual acknowledgment token exchange (MATE) and provide additional analysis on the impact of the chosen tokens. Building upon those insights, we propose mutually endorsed distributed incentive acknowledgment token exchange (MEDIATE), an extended PI architecture employing automatic token derivation via decentralized consensus. Empirical results show the stable agreement on appropriate tokens yielding superior performance compared to static tokens and state-of-the-art approaches in different social dilemma environments with various reward distributions.

Although the synthesis of programs encoding policies often carries the promise of interpretability, systematic evaluations were never performed to assess the interpretability of these policies, likely because of the complexity of such an evaluation. In this paper, we introduce a novel metric that uses large-language models (LLM) to assess the interpretability of programmatic policies. For our metric, an LLM is given both a program and a description of its associated programming language. The LLM then formulates a natural language explanation of the program. This explanation is subsequently fed into a second LLM, which tries to reconstruct the program from the natural-language explanation. Our metric then measures the behavioral similarity between the reconstructed program and the original. We validate our approach with synthesized and human-crafted programmatic policies for playing a real-time strategy game, comparing the interpretability scores of these programmatic policies to obfuscated versions of the same programs. Our LLM-based interpretability score consistently ranks less interpretable programs lower and more interpretable ones higher. These findings suggest that our metric could serve as a reliable and inexpensive tool for evaluating the interpretability of programmatic policies.

In real-world environments, autonomous agents rely on their egocentric observations. They must learn adaptive strategies to interact with others who possess mixed motivations, discernible only through visible cues. Several Multi-Agent Reinforcement Learning (MARL) methods adopt centralized approaches that involve either centralized training or reward-sharing, often violating the realistic ways in which living organisms, like animals or humans, process information and interact. MARL strategies deploying decentralized training with intrinsic motivation offer a self-supervised approach, enable agents to develop flexible social strategies through the interaction of autonomous agents. However, by contrasting the self-supervised and centralized methods, we reveal that populations trained with reward-sharing methods surpass those using self-supervised methods in a mixed-motive environment. We link this superiority to specialized role emergence and an agent's expertise in its role. Interestingly, this gap shrinks in pure-motive settings, emphasizing the need for evaluations in more complex, realistic environments (mixed-motive). Our preliminary results suggest a gap in population performance that can be closed by improving self-supervised methods and thereby pushing MARL closer to real-world readiness.

Crop management decision support systems are specialized tools for farmers that reduce the riskiness of revenue streams, especially valuable for use under the current climate changes that impact agricultural productivity. Unfortunately, small farmers in India, who could greatly benefit from these tools, do not have access to them. In this paper, we model an individual greenhouse as a Markov Decision Process (MDP) and adapt Li and Li (2019)'s Follow the Weighted Leader (FWL) online learning algorithm to offer crop planning advice. We successfully produce utility-preserving cropping pattern suggestions in simulations. When we compare against an offline planning algorithm, we achieve the same cumulative revenue with greatly reduced runtime.