A market based approach for resolving resource constrained task allocation problems in a software development process

We consider software development as an economic activity, where goods and services can be modeled as a resource constrained task allocation problem. This paper introduces a market based mechanism to overcome task allocation issues in a software development process. It proposes a mechanism with a prescribed set of rules, where valuation is based on the behaviors of stakeholders such as biding for a task. A bid process ensures that a stakeholder, who values the resource most, will have it allocated for a limited number of times. To observe the bidders behaviors, we initiate an approach incorporated with a process simulation model. Our preliminary results support the idea that our model is useful for optimizing the value based task allocations, creating a market value for the project assets, and for achieving proper allocation of project resources specifically on large scale software projects

A data-driven game theoretic strategy for developers in software crowdsourcing: a case study

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward

Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching

Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems

Can islands of effectiveness thrive in difficult governance settings ? the political economy of local-level collaborative governance

Many low-income countries contend with a governance syndrome characterized by a difficult combination of seeming openness, weak institutions, and strong inter-elite contestation for power and resources. In such countries, neither broad-based policy nor public management reforms are likely to be feasible. But are broad-based approaches necessary? Theory and evidence suggest that in such settings progress could be driven by"islands of effectiveness"-- narrowly-focused initiatives that combine high-quality institutional arrangements at the micro-level, plus supportive, narrowly-targeted policy reforms. This paper explores whether and how local-level collaborative governance can provide a platform for these islands of effectiveness. Drawing on the analytical framework developed by the Nobel-prize winning social scientist Elinor Ostrom, the paper reviews the underpinnings of successful collaborative governance. It introduces a simple model for exploring the interactions between collaborative governance and political economy. The model highlights the conditions under which coordination is capable of countering threats from predators seeking to capture the returns from collaborative governance for themselves. The relative strength in the broader environment of two opposing networks emerges as key --"threat networks"to which predators have access, and countervailing"trumping networks"on which protagonists of effective collaborative governance can draw. The paper illustrates the potential practical relevance of the approach with three heuristic examples: the governance of schools, fisheries, and road construction and maintenance. It concludes by laying out an agenda for further empirical research, and suggesting what might be the implications of the approach for future operational practice.Governance Indicators,National Governance,Public Sector Corruption&Anticorruption Measures,Environmental Economics&Policies,Economic Policy, Institutions and Governance