Food and nutrition insecurity is a growing global challenge, exacerbated by crises like the COVID-19 pandemic and disruptions in supply chains caused by geopolitical events. This study introduces a hybrid Web3-Web2 system to enhance food security through monitoring the transaction-based food system at a national level. The system comprises elements based on blockchain technology and business intelligence (BI) in the state of Israel. By combining the decentralization, transparency, and immutability of blockchain with some functionalities of Web2 applications, the system enables real-time monitoring and optimization of Israel’s food supply chain. The blockchain component, a natural candidate for storing transaction-based data, ensures data integrity, traceability, and trust among stakeholders, while the BI dashboards facilitate data-driven decision-making and efficient resource allocation. The system implements smart contracts to automate compliance verification while maintaining transaction privacy through strategic data partitioning between public and private storage. The system also leverages graph-based network analysis to identify inefficiencies, minimize food waste, and enhance supply chain sustainability. We discuss how technology can address food security challenges and outline pathways for implementation, offering a model for other nations facing similar issues.

When users stand to gain from certain predictive outcomes, they are prone to act strategically to obtain predictions that are favorable. Most current works consider strategic behavior that manifests as users modifying their features; instead, we study a novel setting in which users decide whether to even participate (or not), this in response to the learned classifier. Considering learning approaches of increasing strategic awareness, we investigate the effects of user self-selection on learning, and the implications of learning on the composition of the self-selected population. Building on this, we propose a differentiable framework for learning under self-selective behavior, which can be optimized effectively. We conclude with experiments on real data and simulated behavior that complement our analysis and demonstrate the utility of our approach.

Motivated by the high discard rate of donated organs in the United States, we study an allocation problem in the presence of learning and strategic incentives. We consider a setting where a benevolent social planner decides whether and how to allocate a single indivisible object to a queue of strategic agents. The object has a common true quality, good or bad, which is ex-ante unknown to everyone. Each agent holds an informative, yet noisy, private signal about the quality. To make a correct allocation decision the planner attempts to learn the object quality by truthfully eliciting agents' signals. Under the commonly applied sequential offering mechanism, we show that learning is hampered by the presence of strategic incentives as herding may emerge. This can result in incorrect allocation and welfare loss. To overcome these issues, we propose a novel class of incentive-compatible mechanisms. Our mechanism involves a batch-by-batch, dynamic voting process using a majority rule. We prove that the proposed voting mechanisms improve the probability of correct allocation whenever agents are sufficiently well informed. Particularly, we show that such an improvement can be achieved via a simple greedy algorithm. We quantify the improvement using simulations.

Seed fundraising for ventures often takes place by sequentially approaching potential contributors, whose decisions are observed by other contributors. The fundraising succeeds when a target number of investments is reached. When a single investment suffices, this setting resembles the classic information cascades model. However, when more than one investment is needed, the solution is radically different and exhibits surprising complexities. We analyze a setting where contributors' levels of information are i.i.d. draws from a known distribution, and find strategies in equilibrium for all. We show that participants rely on social insurance,i.e., invest despite having unfavorable private information, relying on future player strategies to protect them from loss. Delegationis an extreme form of social insurance where a contributor will unconditionally invest, effectively delegating the decision to future players. In typical fundraising, early contributors will invest unconditionally, stopping when the target is "close enough", thus de factodelegating the business of determining fundraising success or failure to the last contributors.

Society uses the following game to decide on the supply of a public good. Each agent can choose whether or not to contribute to the good. Contributions are collected and the good is supplied whenever total contributions exceed a threshold. We study the case where the public good is excludable, agents have a common value and each agent receives a private signal about the common value. This game models a standard crowdfunding setting as it is executed in popular crowdfunding platforms such as Kickstarter and Indiegogo. We study how well crowdfunding performs from the firm's perspective, in terms of market penetration, and how it performs from the perspective of society, in terms of efficiency.