City Branding: An Austin Case Study

Globalization and changing power dynamics amongst nation-states has created new opportunities for smaller entities, like cities, to partake in international affairs and discussions. While global cities already have established economic and political roles on a global level, city leaders and stakeholders in emerging cities around the world have begun looking for ways to capitalize on their economic and political potential. One viable strategy for achieving this is city branding. The branding process helps highlight place-based attributes and collective experiences of people within the city in order to make a city more competitive while simultaneously creating a clear path forward. The city of Austin is a prime candidate for such a project because it is currently experiencing a crisis of identity at a critical growth point. After several years of rapid economic growth, city leaders and the stakeholders need to reassess the priorities and values of the city to ensure a clear path forward both at the local and global level. A city branding project would not only help contend with the competing visions that are found within the city but also help navigate the pitfalls created by such rapid growth. This thesis presents the tagline “In this Together” for Austin because not only does it help align stakeholders and local leaders under one prevailing idea, it also builds in the flexibility and innovation that has become emblematic of the city. It helps reframe the city’s priorities to ensure that the people are at the forefront because they ultimately ensure the legitimacy of the brand. Essentially, the idea here is that city branding can offer a framework within which important conversations can be had by city leaders and stakeholders about the priorities and values of a city and how those translate to real actions and change that happen at both the local and global level.Anthropolog

Bootstrapping Trust with Partial and Subjective Observability

Assessment of trust and reputation typically relies on prior experiences of a trustee agent, which may not exist, e.g. especially in highly dynamic environments. In these cases stereotypes can be used, where traits of trustees can be used as an indicator of their behaviour during interactions. Communicating observations of traits to witnesses who are unable to observe them is difficult, however, when the traits are interpreted subjectively. In this paper we propose a mechanism for learning translations between such subjective observations, evaluating it in a simulated marketplace

Stereotype reputation with limited observability

Assessing trust and reputation is essential in multi-agent systems where agents must decide who to interact with. Assessment typically relies on the direct experience of a trustor with a trustee agent, or on information from witnesses. Where direct or witness information is unavailable, such as when agent turnover is high, stereotypes learned from common traits and behaviour can provide this information. Such traits may be only partially or subjectively observed, with witnesses not observing traits of some trustees or interpreting their observations differently. Existing stereotype-based techniques are unable to account for such partial observability and subjectivity. In this paper we propose a method for extracting information from witness observations that enables stereotypes to be applied in partially and subjectively observable dynamic environments. Specifically, we present a mechanism for learning translations between observations made by trustor and witness agents with subjective interpretations of traits. We show through simulations that such translation is necessary for reliable reputation assessments in dynamic environments with partial and subjective observability

Reputation-based provider incentivisation for provenance provision

Knowledge of circumstances under which past service provisions have occurred enables clients to make more informed selection decisions regarding their future interaction partners. Service providers, however, may often be reluctant to release such circumstances due to the cost and effort required, or to protect their interests. In response, we introduce a reputation-based incentivisation framework, which motivates providers towards the desired behaviour of reporting circumstances via influencing two reputation-related factors: the weights of past provider interactions, which directly impact the provider’s reputation estimate, and the overall confidence in such estimate

Bootstrapping trust with partial and subjective observability

Assessment of trust and reputation typically relies on prior experiences of a trustee agent, which may not exist, e.g. especially in highly dynamic environments. In these cases stereotypes can be used, where traits of trustees can be used as an indicator of their behaviour during interactions. Communicating observations of traits to witnesses who are unable to observe them is difficult, however, when the traits are interpreted subjectively. In this paper we propose a mechanism for learning translations between such subjective observations, evaluating it in a simulated marketplace

Adaptive Composition in Dynamic Service Environments

Due to distribution, participant autonomy and lack of local control, service-based systems operate in highly dynamic and uncertain environments. In the face of such dynamism and volatility, the ability to manage service changes and exceptions during composite service execution is a vital requirement. Most current adaptive composition approaches, however, fail to address service changes without causing undesirable disruptions in execution or considerably degrading the quality of the composite application. In response, this paper presents a novel adaptive execution approach, which efficiently handles service changes occurring at execution time, for both repair and optimisation purposes. The adaptation is performed as soon as possible and in parallel with the execution process, thus reducing interruption time, increasing the chance of a successful recovery, and producing the most optimal solution according to the current environment state. The effectiveness of the proposed approach is demonstrated both analytically and empirically through a case study evaluation applied in the framework of learning object composition. In particular, the results show that, even with frequent changes (e.g. 20 changes per service execution), or in the cases where interference with execution is non-preventable (e.g., when an executed service delivers unanticipated quality values), our approach manages to recover from the situation with minimal interruption

A vision for socially incentivised recommendations

Typically, recommender systems focus solely on individual preferences of users or small groups of users, but recommendations can have effects on the wider social structure. Social considerations are there- fore necessary in recommendation generation. In this paper, we identify gaps in literature relevant to socially responsible recommendation sys- tems, and present a number of challenges. Finally, we present a vision for an architecture capable of generating socially responsible recommen- dations and encouraging their acceptance via incentives and rationales

A Reputation-based Framework for Honest Provenance Reporting

Given the distributed, heterogenous, and dynamic nature of service-based IoT systems, capturing circumstances data underlying service provisions becomes increasingly important for understanding process flow and tracing how outputs came about, thus enabling clients to make more informed decisions regarding future interaction partners. Whilst service providers are the main source of such circumstances data, they may often be reluctant to release it, e.g. due to the cost and effort required, or to protect their interests. In response, this paper introduces a reputation-based framework, guided by intelligent software agents, to support the sharing of truthful circumstances information by providers. In this framework, assessor agents, acting on behalf of clients, rank and select service providers according to reputation, while provider agents, acting on behalf of service providers, learn from the environment and adjust provider’s circumstances provision policies in the direction that increases provider profit with respect to perceived reputation. The novelty of the reputation assessment model adopted by assessor agents lies in affecting provider reputation scores by whether or not they reveal truthful circumstances data underlying their service provisions, in addition to other factors commonly adopted by existing reputation schemes. The effectiveness of the proposed framework is demonstrated through an agent-based simulation including robustness against a number of attacks, with a comparative performance analysis against FIRE as a baseline reputation model

A reputation-based framework for honest provenance reporting

Given the distributed, heterogenous, and dynamic nature of service-based IoT systems, capturing circumstances data underlying service provisions becomes increasingly important for understanding process flow and tracing how outputs came about, thus enabling clients to make more informed decisions regarding future interaction partners. Whilst service providers are the main source of such circumstances data, they may often be reluctant to release it, e.g. due to the cost and effort required, or to protect their interests. In response, this paper introduces a reputation-based framework, guided by intelligent software agents, to support the sharing of truthful circumstances information by providers. In this framework, assessor agents, acting on behalf of clients, rank and select service providers according to reputation, while provider agents, acting on behalf of service providers, learn from the environment and adjust provider’s circumstances provision policies in the direction that increases provider profit with respect to perceived reputation. The novelty of the reputation assessment model adopted by assessor agents lies in affecting provider reputation scores by whether or not they reveal truthful circumstances data underlying their service provisions, in addition to other factors commonly adopted by existing reputation schemes. The effectiveness of the proposed framework is demonstrated through an agent-based simulation including robustness against a number of attacks, with a comparative performance analysis against FIRE as a baseline reputation model