Social Innovation in Small‐Scale Blue Food Systems: A Case Study of Oyster Harvesters in The Gambia, West Africa

ABSTRACT The emerging “Blue Economy” and “Blue Growth” paradigms, focusing on economic growth, innovations, and environmental sustainability, have increasingly dominated discussions on marine and coastal development. However, in this discourse, the future of small‐scale blue food systems often remains underemphasized and increasingly uncertain. This paper explores the potential of social innovation approaches as tools to support a collective and inclusive transformation within blue food systems in the blue economy. We draw on a case study of a female‐led social enterprise in The Gambia—the TRY Oyster Women's Association (TRY)—to highlight the social innovation pathways for small‐scale blue food systems transformation. The study shows that social innovation through institutional changes, participatory governance, emerging institutional entrepreneurs, and financial resource mobilization and support facilitates effective natural resources management, environmental stewardship, and social and economic inclusion within small‐scale blue food systems. Importantly, the granting of TRY's exclusive user rights through a national Fishery Act has facilitated community engagement in sustainable management of the oyster shellfish and mangroves in The Gambia. Also, TRY promotes community empowerment and social cohesion through social learning and capacity‐building initiatives with financial and technical support from external partners enabling the association to thrive as a social enterprise. The paper underscores the significance of social innovation in steering successful transformation within small‐scale blue food systems, fostering environmental and inclusive resource management in the blue economy with applicability in similar geographical contexts

Heterogeneity in health insurance choice: An experimental investigation of consumer choice and feature preferences

Abstract We investigate heterogeneity in health insurance choice using data from a controlled laboratory experiment. Participants make consecutive choices from sets of insurance plans that vary in premium, deductible, and complementary coverage of illnesses. We find that there is considerable heterogeneity in how much individuals are willing to pay for certain plan attributes. To better understand these differences, we account for individual risk preferences using a rank‐dependent expected utility (RDEU) model and assess the welfare effects of plan choices. At the aggregate level, we find welfare losses under both the normative RDEU model and the descriptive EV model. At the individual level, however, the results are more differentiated: for some individuals, choices are consistent with their RDEU preferences, whereas for others, choices do not fit either model, suggesting either decision errors or reliance on heuristics

A generalised comparison of Pareto/NBD based forecasts using MCMC, maximum likelihood, and heuristics

Abstract This study is the first generalised effort to compare the forecasting efficacy of different Pareto/NBD model-based forecasts. Monte Carlo Markov Chain (MCMC)-based estimates, Maximum Likelihood Estimation (MLE), and heuristics are applied to four different types of forecasts: (1) predicting the future number of purchases a single customer makes within a given period, (2) identifying active customers who will make at least one purchase within a given period, (3) identifying the customers who will belong to the top segments of the customer base, and (4) predicting the timing of a customer's next purchase. The results show that the model-based forecasts outperform the heuristics regarding predictive power and accuracy for the first three types of forecasts. MCMC yields slightly better results than MLE and it can additionally convince with confidence intervals for the number of future purchases. Forecasting the timing of a customer’s next purchase yields deviations that are too large to be used in practice.C11;C15;C52;C53;C6

The Economics of an Import Tariff in the Keynesian Model: An Intermediate Macroeconomics Treatment

The standard textbook treatment of expansionary fiscal policy at intermediate macroeconomics level, e.g., Blanchard (2024), Burda and Wyplosz (2023), only consider taxes affecting the economy through the consumption function, by increasing the level of disposable income. Motivated by recent events - the import tariffs introduced in the US by Trump administration - in this paper we introduce such tariffs to explore how they work in the Keynesian cross framework. As expected, an increase in import tariffs stimulates aggregate demand, which is the ”import substitution effect” from the trade literature. There is also a multiplier effect, which we refer to the ”import tariff multiplier effect.” This possible stimulus effect on the domestic (US) economy from an increase in the import tariff rate is of interest to policy-makers, and in developing countries with a public finance model organized around trade taxation, or countries that follow an export-led growth model by discouraging import

Reinforcement learning versus data-driven dynamic programming: a comparison for finite horizon dynamic pricing markets

Abstract Revenue management (RM) plays a vital role to optimize sales processes in real-life applications under incomplete information. The prediction of consumer demand and the anticipation of price reactions of competitors became key factors in RM to be able to apply classical dynamic programming (DP) methods for expected long-term reward maximization. Modern model-free deep Reinforcement Learning (RL) approaches are able to derive optimized policies without explicit estimations of underlying model dynamics. However, RL algorithms typically require either vast amounts of training data or a suitable synthetic model to be trained on. As existing studies focus on one group of algorithms only, the relation between established DP approaches and new RL techniques is opaque. To address this issue, in this paper, we use a dynamic pricing framework for an airline ticket market to compare state-of-the-art RL algorithms and data-driven versions of classic DP methods regarding (i) performance and (ii) required data to each other. For the DP techniques, we use estimations of market dynamics to be able to compare their performance and data consumption against RL methods. The numerical results of our experiments, which include monopoly as well as duopoly markets, allow to study how the different approaches’ performances relate to each other in exemplary settings. In both setups, we find that with few data (about 10 episodes) fitted DP methods were highly competitive; with medium amounts of data (about 100 episodes) DP methods got outperformed by RL, where PPO provided the best results. Given large amounts of training data (about 1000 episodes), the best RL algorithms, i.e., TD3, DDPG, PPO, and SAC, performed similarly achieving about 90% and more of the optimal solution

Following the blind? Database coding policies and the case of IFRS noncompliance

Abstract We present a case illustrating the pitfalls of insufficient disclosure of commercial databases' coding policies. We replicate the finding in the literature that a nontrivial percentage of firms mandated to adopt IFRS ignore this obligation. Specifically, Pownall and Wieczynska (2018, Contemporary Accounting Research , 35 (2), 1029–1066) report more than 3,000 cases, or 10% of all mandated firms in the European Union. When using primary data sources (applicable local regulations and firms' annual reports), we find that noncompliance with IFRS adoption is nonexistent in the one‐to‐one replication using the same firm‐year observations. We attribute the prior misperception to the commercial database's insufficient disclosure of a misleading coding policy of the consolidation item. We also show that no other data provider correctly captures consolidation status, which determines whether firms must report under IFRS. In response to this gap, we showcase the application of bidirectional encoder representations from transformers (BERT) models for extracting the consolidation status and offer guidance for coding IFRS‐mandated firms. Our article underscores the need to exercise caution when using secondary data sources

Power contestation and regulation in digital platform ecosystems - The case of the EU’s Digital Markets Act

Abstract In this study, we investigate how power contestation between complementors and powerful platform owners unfolds in digital platform ecosystems as they come under regulatory scrutiny. Platform owners like Alphabet/Google, Apple, Amazon, and Meta/Facebook dominate their respective ecosystems, leveraging network effects and market power to maintain their positions, giving them a powerful advantage vis-à-vis supply-side complementors. Complementors have become increasingly dependent on these platforms and struggle due to their weakened position. In the EU, the European Commission has implemented the Digital Markets Act, which aims to increase fairness and contestability within and across digital platform ecosystems. Using a case study approach, we analyze how regulatory scrutiny, such as the Digital Markets Act, has affected power contestation between platform owners and complementors. In particular, we investigate app store and search platform ecosystems. We establish a conceptual framework of power contestation that shows how complementors and platform owners interact directly and through the regulator as a mediator, vying for power in the ecosystem. We thereby contribute to the emerging Information Systems literature on power dynamics and the regulation of digital platform ecosystems.L500;M15

The extractive business model of private equity firms in the German healthcare sector and the crisis in social reproduction

Abstract The paper explores the contradiction between the extractive business model of the private equity buy-outs of the German healthcare sector and the crisis of reproduction, which has the potential not only to undermine the solidaristic healthcare system, but also to undermine the idea of societal protection as the object of socio-economic governance. Drawing on the insights of the international/comparative political economy and feminist literature opens an analytical space to connect the macro-financialized economy to the non-economic sphere of social reproduction and, in the process, uncover the contradictory nature of this relationship. From the vantage point of the financial crisis of 2007, the private equity industry was the beneficiary of the consecutive switch of major central banks to unconventional monetary policy and exceptionally low interest rates swamping the market with liquidity. Private equity managers and their shareholders are the big winners due to their extraordinary financial power, but this has dire consequences for the (mostly female) staff, the patients, and the wider solidaristic healthcare community. Moreover, since private equity firms operate through complex and opaque international holding structures, these entities have little to fear from German national regulatory powers to ban or prohibit tools of financial engineering such as debt push-down, asset stripping, and tax evasion; this creates a high level of uncertainty as to the impact of PEs on the sustainability of the German social market economy as a whole.B54;B59;D60;E00;E52;G23;I1

Lessons from the EU Effort Sharing Decision for Supranational Climate Cooperation: A Firm-Level Analysis

Abstract As an example of supranational climate policy coordination for sectors not covered by carbon trading, the European Effort Sharing Decision set national targets for emission reductions for the time period 2013–2020. Member States were free to decide the national policies to implement to achieve these objectives. This is the first quantification of the impact this regulation had on the emissions of firms in the corresponding sectors. We exploit the differences along three variables: a national-level treatment intensity, an exposure index defined at the firm level and a time dimension (before or after the introduction of the policy). We find that, even in countries with no stringent target, emissions from exposed firms tended to decrease more than emissions from non-exposed firms. In addition, each percentage point increase in the stringency of the treatment leads to a 5.7% reduction in emissions for an average exposed firm. This provides interesting insights for other supranational climate agreements.D22;F53;L51;Q54;Q5

Mixed-integer linearity in nonlinear optimization: a trust region approach

Abstract Bringing together nonlinear optimization with polyhedral and integrality constraints enables versatile modeling, but poses significant computational challenges. We investigate a method to address these problems based on sequential mixed-integer linearization with trust region safeguard, computing feasible iterates via calls to a generic mixed-integer linear solver. Convergence to critical, possibly suboptimal, feasible points is established for arbitrary starting points. Finally, we present numerical applications in nonsmooth optimal control and optimal network design and operation