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on Project, Program and Portfolio Management |
| By: | Yurun Ge; Lucas B\"ottcher; Tom Chou; Maria R. D'Orsogna |
| Abstract: | How to allocate limited resources to projects that will yield the greatest long-term benefits is a problem that often arises in decision-making under uncertainty. For example, organizations may need to evaluate and select innovation projects with risky returns. Similarly, when allocating resources to research projects, funding agencies are tasked with identifying the most promising proposals based on idiosyncratic criteria. Finally, in participatory budgeting, a local community may need to select a subset of public projects to fund. Regardless of context, agents must estimate the uncertain values of a potentially large number of projects. Developing parsimonious methods to compare these projects, and aggregating agent evaluations so that the overall benefit is maximized, are critical in assembling the best project portfolio. Unlike in standard sorting algorithms, evaluating projects on the basis of uncertain long-term benefits introduces additional complexities. We propose comparison rules based on Quicksort and the Bradley--Terry model, which connects rankings to pairwise "win" probabilities. In our model, each agent determines win probabilities of a pair of projects based on his or her specific evaluation of the projects' long-term benefit. The win probabilities are then appropriately aggregated and used to rank projects. Several of the methods we propose perform better than the two most effective aggregation methods currently available. Additionally, our methods can be combined with sampling techniques to significantly reduce the number of pairwise comparisons. We also discuss how the Bradley--Terry portfolio selection approach can be implemented in practice. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.16093 |
| By: | Qiaoyi Chen; Nicholas Ryan; Daniel Xu |
| Abstract: | We study carbon offsets sold by firms in China under the Clean Development Mechanism (CDM). We find that offset-selling firms, meant to cut carbon emissions, instead increase them by 49% after starting an offset project. In a model of firm investment decisions and offset review, we estimate that CDM firms increase emissions due to both the selection of higher-growth firms into projects (35 pp) and because offset projects themselves boost firm growth and therefore emissions (14 pp). The CDM reduces global surplus by causing damages from increased emissions four times greater than private gains from trade in the offset market. |
| JEL: | L51 O13 Q54 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33636 |
| By: | Apró, William Zoltán |
| Abstract: | Abstract Traditional IT project forecasting methods rely on siloed, retrospective data (e.g., Jira ticket histories), leaving teams unprepared for evolving risks such as shifting customer demands, accumulating technical debt, or new regulatory mandates. Studies show that 60% of IT projects exceed budgets due to unplanned scope changes, exposing the limitations of reactive approaches. We introduce Agentic AI for Proactive IT Forecasting (AAPIF), a novel framework that integrates intelligence-grade premise valuation with multi-source data fusion to proactively forecast project outcomes across technical, business, and market contexts. Unlike static models, AAPIF dynamically weights input data—such as customer requirements, organizational context, and compliance signals—based on reliability (freshness, credibility) and relevance (contribution weights C_i). It continuously refines predictions using reinforcement learning. Key Contributions: A mathematical model computing confidence-weighted success probabilities, achieving 89% accuracy—a 32% improvement over Random Forest baselines. Actionable intelligence protocols that reduce data collection errors by 45%, utilizing premise valuation (e.g., stakeholder alignment scoring) and automated risk alerts. In a fintech case study, AAPIF reduced unplanned scope changes by 37% through risk prediction (e.g., "72% likelihood of API scalability issues in Q3") and strategic recommendations (e.g., "Reassign three developers to refactor modules"). By transforming raw data into strategic foresight, AAPIF empowers project managers to become proactive architects of success, rather than reactive trouble-shooters. Keywords: Agentic AI, IT project forecasting, premise valuation, Agile project management, predictive analytics, risk mitigation |
| Date: | 2025–04–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:jtvqu_v1 |
| By: | Condie, Abbie; Bakhtawar, Alsa; Ireland, Erika; Afroz, Farhana; Hathiari, Neha; kyzy, Syrga Kanatbek; Erdenebat, Munkhshur; Mohammad, Gazi Golam; Das, Utpal Kumar; Meek, Kristin |
| Abstract: | Urban resilience has become a central theme in addressing the complex challenges faced by rapidly growing cities. The Asia Resilient Cities (ARC) Project aims to foster resilience through a co-creation approach that combines participatory systems mapping (PSM) and community engagement. This paper outlines ARC’s methodology, which integrates diverse stakeholder perspectives, including marginalized groups, into urban resilience planning in four cities: Rajkot, India; Khulna, Bangladesh; Bishkek, Kyrgyz Republic; and Ulaanbaatar, Mongolia. The paper discusses how ARC adapted lessons from previous projects, emphasizing early and meaningful resident involvement to shape work plans that reflect the lived realities of city residents. Initial results highlight both the strengths and learnings from integrating resident feedback into resilience strategies, demonstrating how co-creation can align technical expertise with local context to create more inclusive, actionable plans. Key themes include the role of systems thinking, multistakeholder participation, and adaptive learning in urban planning. The findings underscore the need for contextualized, collaborative approaches to address the “wicked problems” inherent in urban development and resilience building. |
| Date: | 2025–04–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:c8w2d_v1 |
| By: | Kym Pram; Burkhard C. Schipper |
| Abstract: | We study the design of efficient mechanisms under asymmetric awareness and information. Unawareness refers to the lack of conception rather than the lack of information. Assuming quasi-linear utilities and private values, we show that we can implement in conditional dominant strategies a social choice function that is utilitarian ex-post efficient when pooling all awareness of all agents without the need of the social planner being fully aware ex-ante. To this end, we develop novel dynamic versions of Vickrey-Clarke-Groves mechanisms in which types are revealed and subsequently elaborated at endogenous higher awareness levels. We explore how asymmetric awareness affects budget balance and participation constraints. We show that ex-ante unforeseen contingencies are no excuse for deficits. Finally, we propose a modified reverse second price auction for efficient procurement of complex incompletely specified projects. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.04382 |
| By: | Zhangfeng Jin (Zhejiang University of Technology); Klaus Prettner (Department of Economics, Vienna University of Economics and Business) |
| Abstract: | This paper examines the impact of technology transfers on long-term innovation. We propose an extended Schumpeterian growth framework to characterize the channels by which technology transfers impact on innovation. Exploiting variations in the adoption of Soviet-aided industrialization programs across Chinese cities, we find that firms located in cities affected by 156 major industrial projects of the Soviet Union witness fewer Investments in research and development on average after nearly half a century. The effect is particularly pronounced for non-state-owned firms. The decline in innovation inputs is further supported by a lower probability of patenting in these localities. A likely underlying mechanism is the low adoption of performance-based reward systems that influence labor reallocation within firms, rather than inadequate capital and skilled workers. Despite prior successes during the planned economy era, the adoption of such foreign aid tends to impede innovation as China transitions towards a more market-oriented economy. |
| Keywords: | Foreign Aid, Technology Transfers, Innovation Inputs, Pay for Performance, China |
| JEL: | F35 O30 M52 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:wiw:wiwwuw:wuwp379 |
| By: | Moustafa, Khaled (Founder & Editor of ArabiXiv) |
| Abstract: | Some companies in the digital and academic sectors put profits above integrity, using coercive tactics like creating author profiles without consent, mandating ORCID use, and selling personal data. ResearchGate, for instance, generates authors profiles without their knowledge or approval. Research Square, another commercial platform, requires authors to obligatory share manuscripts and private information prior to submit their work to journals. Meanwhile, tech firms and software makers engineer product incompatibilities, pushing users to upgrade or discard still-functional devices. Likewise, grant applications often depend on institutional ties, wrongly implying that valuable projects cannot thrive independently. Such restrictions can exclude innovative work that could benefit science and society. These practices, among others, appear to value financial gain over scientific integrity, transparency and ethics. Reconsideration is required. |
| Date: | 2025–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:gew8u_v1 |