8,942 research outputs found

    Learning in rent-seeking contests with payoff risk and foregone payoff Information

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    We test whether deviations from Nash equilibrium in rent-seeking contests can be explained by the slow convergence of payoff-based learning. We identify and eliminate two noise sources that slow down learning: first, opponents are changing their actions across rounds; second, payoffs are probabilistic, which reduces the correlation between expected and realized payoffs. We find that average choices are not significantly different from the risk-neutral Nash equilibrium predictions only when both noise sources are eliminated by supplying foregone payoff information and removing payoff risk. Payoff-based learning can explain these results better than alternative theories. We propose a hybrid learning model that combines reinforcement and belief learning with risk, social and other preferences, and show that it fits data well, mostly because of reinforcement learning

    METROPOLITAN ENCHANTMENT AND DISENCHANTMENT. METROPOLITAN ANTHROPOLOGY FOR THE CONTEMPORARY LIVING MAP CONSTRUCTION

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    We can no longer interpret the contemporary metropolis as we did in the last century. The thought of civil economy regarding the contemporary Metropolis conflicts more or less radically with the merely acquisitive dimension of the behaviour of its citizens. What is needed is therefore a new capacity for imagining the economic-productive future of the city: hybrid social enterprises, economically sustainable, structured and capable of using technologies, could be a solution for producing value and distributing it fairly and inclusively. Metropolitan Urbanity is another issue to establish. Metropolis needs new spaces where inclusion can occur, and where a repository of the imagery can be recreated. What is the ontology behind the technique of metropolitan planning and management, its vision and its symbols? Competitiveness, speed, and meritocracy are political words, not technical ones. Metropolitan Urbanity is the characteristic of a polis that expresses itself in its public places. Today, however, public places are private ones that are destined for public use. The Common Good has always had a space of representation in the city, which was the public space. Today, the Green-Grey Infrastructure is the metropolitan city's monument that communicates a value for future generations and must therefore be recognised and imagined; it is the production of the metropolitan symbolic imagery, the new magic of the city

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Affinity-Based Reinforcement Learning : A New Paradigm for Agent Interpretability

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    The steady increase in complexity of reinforcement learning (RL) algorithms is accompanied by a corresponding increase in opacity that obfuscates insights into their devised strategies. Methods in explainable artificial intelligence seek to mitigate this opacity by either creating transparent algorithms or extracting explanations post hoc. A third category exists that allows the developer to affect what agents learn: constrained RL has been used in safety-critical applications and prohibits agents from visiting certain states; preference-based RL agents have been used in robotics applications and learn state-action preferences instead of traditional reward functions. We propose a new affinity-based RL paradigm in which agents learn strategies that are partially decoupled from reward functions. Unlike entropy regularisation, we regularise the objective function with a distinct action distribution that represents a desired behaviour; we encourage the agent to act according to a prior while learning to maximise rewards. The result is an inherently interpretable agent that solves problems with an intrinsic affinity for certain actions. We demonstrate the utility of our method in a financial application: we learn continuous time-variant compositions of prototypical policies, each interpretable by its action affinities, that are globally interpretable according to customers’ financial personalities. Our method combines advantages from both constrained RL and preferencebased RL: it retains the reward function but generalises the policy to match a defined behaviour, thus avoiding problems such as reward shaping and hacking. Unlike Boolean task composition, our method is a fuzzy superposition of different prototypical strategies to arrive at a more complex, yet interpretable, strategy.publishedVersio

    Presentation, Technology, and Content – Studies on Consumer Behaviour in Journalism

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    The goal of this thesis is to extend the understanding of the effects the presentation and visuality of journalism can have on users and consumers. Further, this thesis makes a case that a focus on the presentation and visuality of journalism is a possibility for audience orientation without compromising journalistic quality. The visual presentation of journalism has become very important because of technological developments that make the reproduction of design, pictures, layout and any other relevant presentation modes so much easier. While practitioners are handling this on a daily basis, management researchers are just starting to empirically investigate related phenomena, especially in the context of journalism. Along five empirical studies conducted in the journalism field, this thesis establishes links between the presentation, technology and content of journalism and consumer behaviour. It further identifies frameworks to approach the presentation of journalism and theoretically explains how the presentation can provide a possibility for audience orientation without compromising content. Thereupon, this research derives recommendations for theory and practitioners in order to uphold the business viability of news production

    Multi-Modal Self-Supervised Learning for Recommendation

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    The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (eg, visual, textual and acoustic) into the latent user representations. While existing works on multi-modal recommendation exploit multimedia content features in enhancing item embeddings, their model representation capability is limited by heavy label reliance and weak robustness on sparse user behavior data. Inspired by the recent progress of self-supervised learning in alleviating label scarcity issue, we explore deriving self-supervision signals with effectively learning of modality-aware user preference and cross-modal dependencies. To this end, we propose a new Multi-Modal Self-Supervised Learning (MMSSL) method which tackles two key challenges. Specifically, to characterize the inter-dependency between the user-item collaborative view and item multi-modal semantic view, we design a modality-aware interactive structure learning paradigm via adversarial perturbations for data augmentation. In addition, to capture the effects that user's modality-aware interaction pattern would interweave with each other, a cross-modal contrastive learning approach is introduced to jointly preserve the inter-modal semantic commonality and user preference diversity. Experiments on real-world datasets verify the superiority of our method in offering great potential for multimedia recommendation over various state-of-the-art baselines. The implementation is released at: https://github.com/HKUDS/MMSSL.Comment: This paper has been published as a full paper at WWW 202

    An Exploration of Academy Deans' Responsibilities in Five U15 Research-Intensive Universities in Canada: Ambiguities and Managerialism in the Academe - A Mixed Methods Research

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    This study examined the responsibilities of academic deans within five U15 research-intensive universities in Canada as they operate in an increasingly complex environment. The academic deans who are sometimes flaunted as Chief Executive Officers, were found to be consummate academics who transitioned from their academic discipline into administration as middle managers. Academic deans have a dual responsibility in that they are accountable to the senior leadership of their university while being advocates for their colleges. Significantly, the responsibilities of these academic middle managers are central to the achievement of their universities’ strategic objectives. However, the position of the deanship is described by researchers as complex, and the very nature of the duality of the role engenders ambiguities. The ambiguities and complexities of academic deans’ responsibilities are said to be influenced by public sector reforms disguised as managerialism. Some practices espoused by managerialism appear to be integral to universities’ strategies globally, whether as an ideology or through processes and practices. Universities in Canada are also adopting various strategies which are said to be driven by managerialism (Brownlee, 2015). Symptomatic of managerialism are various changes in university governance, including the professionalization of the roles of middle managers, now referred to as chief executive officers in some institutions, and the implementation of marketing techniques (Brownlee, 2015; Kolsaker, 2008; Olssen, 2002). Additionally, and as indicated in the literature, reflective of managerialism are the demands for accountability, efficiency, and effectiveness which are achieved through practices such as increased competition, a focus on marketization, and engagement of private-public partnerships. According to the literature, the practices espoused by managerialism in higher education institutions (Meek et al., 2020; Seale & Cross, 2016) have shifted the responsibilities of academic deans to a type of management that is reflective of corporate-style management practices and evidenced by various corporate terminologies. Given the tenets of managerialism, the argument obtains that some principles of this ideology are translated into practices and have contributed to the evolved roles of academic deans. They now engage in business-like practices, the processes of their institutions’ strategic planning initiatives, establishing public-private partnerships, and marketization, among others. The changes have impacted how academic deans interpret, understand, and enact their roles, which are oftentimes imbued with role conflict and ambiguity due to competing demands and unclear expectations by various constituents (Arntzen, 2016; Boyko & Jones, 2010; Hoyle & Wallace, 2005). With the evolved responsibilities of academic mid-level managers, more specifically academic deans who are at the centre of this study, there is evidence of job enlargement as well as increased complexities in their roles. As such, in examining academic deans’ responsibilities, this study gathered information on academic deans lived experiences and perceptions of the presence of managerialism in their institutions and how their responsibilities reflect practices akin to managerialism. That is, responsibilities that mirror management techniques usually employed by the private sector or corporate organizations. The study further examined academic deans’ perceptions of role conflict and role ambiguity and how their perceived self-efficacy and tolerance-intolerance of ambiguity influence how they navigate the complexities of their roles. The study’s findings were limited to the perceptions of the participants who indicated that some of their responsibilities are reflective of practices such as budgeting and fund development; strategic planning; advancement/fundraising/establishing donor relationships; advertising/marketization and human resource management, among others. According to the narratives provided by the academic deans in this study, they found themselves ill-prepared for important corporate-like responsibilities, which they indicated generally do not coalesce with their academic disciplines. Further, the findings revealed that the practices that characterize the responsibilities of these middle-level managers/chief executive officers are delineated by varying degrees of uncertainties and ambiguities which are defined by role conflict and role ambiguity. However, the academic deans in the study demonstrated that having a sense of self-efficacy and a high tolerance for ambiguity had been valuable in helping them to navigate the complexities of their roles as they engaged the corporate-like management imperatives of their responsibilities. The research was grounded in the constructivist paradigm through a qualitatively dominant cross-over (Frels & Onwuegbuzie, 2013) mixed-methods research design. This process captured the subjective experiences of academic deans to gain an in-depth understanding of the practices of academic deans as they carry out their functions in an ambiguous environment characterized by managerialism (Arntzen, 2016; Ayers, 2012; Bess, 2006). Data were collected to address the research questions using a mixed methods sequential design over two phases. Phase one of this study focused on gathering quantitative data from surveys through SurveyMonkey. Phase two concentrated on the qualitative method of collecting data by way of reviewing position descriptions of academic deans, policy documents governing deans, and elite interviews with deans. The study has implications for further research initiatives, research-into-practice, and contribution to theory. Implications for future research include comparative research with larger sample sizes across U15 research-intensive and non-research-intensive universities to garner a more comprehensive understanding of academic deans’ perceptions of managerialism, role conflict, and role ambiguity. The study findings have potential implications for institutions’ policies governing academic deans’ recruitment and professional development of academics, including the establishment of management career pathways and succession planning initiatives

    Real Estate Investment Trusts (REITs) Corporate Governance and Investment Decision-Making in the United Kingdom, South Africa and Nigeria

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    Adopting Real Estate Investment Trusts (REITs) has been relatively slow due to corporate governance issues and a limited understanding of investment decision-making processes. This study aims to enhance the performance of REITs by developing a Corporate Governance Scoring Framework and improving the investment decision-making process. A mixed-method research strategy was employed to gather data on investment decisionmaking processes and corporate governance in the UK, SA, and Nigeria from 2014-2019. Qualitative data was collected through semi-structured telephone interviews with key decision-makers in the three regimes and analysed using content and discourse analysis techniques. Quantitative data was obtained from the annual financial reports of listed REITs during the study period and analysed using OLS, fixed effects, and random effect models. The Integrated Corporate Governance Index (ICGI), a self-scoring framework, was used to measure the quality of corporate governance strength. The qualitative analysis identified four stages in the investment decision-making process: strategy, search, analysis and adjustment, and consultation or decision and review. The interviews revealed that the board, remuneration, and fee proxies were relevant factors across all three regimes, with audit and ownership also significant in the developing regimes of SA and Nigeria. The board's reputation, experience, and management role were highlighted as crucial during the decision-making process. Performance factors such as 'Operational Stability,' 'Tenant Quality,' 'Experience,' and metrics including 'Rental Income,' 'Dividend Payment,' and 'Yield' were identified. The quantitative analysis demonstrated that adherence to corporate governance codes was highest in the UK, followed by SA and Nigeria. Regression analysis results showed that a higher ICGI score improved return on assets (ROA) and return on equity (ROE) in the UK but not in SA and Nigeria. The index did not significantly impact firm value in the UK and pooled country analysis, but it led to better firm valuation in SA. In the Nigeria REIT regime, the ICGI harmed firm valuation. The study concluded that adherence to country-level corporate governance was more predictive of operational performance than firm valuation. In summary, this study contributes to the existing knowledge by providing insights into the investment decision-making processes of REITs and the importance of corporate governance in improving their performance. The developed Corporate Governance Scoring Framework offers a valuable tool for evaluating the quality of corporate governance in REITs, but further refinement is necessary to keep up with evolving policies

    A Comprehensive Survey on Deep Graph Representation Learning

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    Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future
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