Firewatch: Use of Sattelite Imagery by Remote\ud Communities in Northern Australia for Fire Risk\ud Communications.

This paper presents the contextual background and early findings from a new research project funded by the Australian Research Council titled Using community engagement and enhanced visual information to promote FireWatch satellite communications as a support for collaborative decision-making. FireWatch (provided by Landgate in Western Australia) is an internet-based public information service based on near real time satellite data showing timely information relevant to bushfire safety within Australia. However, it has been developed in a highly technical environment and is currently used chiefly by\ud experts. This project aims to redesign FireWatch for ordinary users and to engage a remote community in Northern Australia in this process, leading to improved decision making surrounding bushfire risk

Prescriptive Analytics in Electricity Markets

Electricity markets are a clear example of a sector in which decision making plays a crucial role in its daily activity. Moreover, uncertainty is intrinsic to electricity markets and affects most of the tasks that agents operating in them must carry out. Many of these tasks involve decisions characterized by low risk and being addressed periodically. In this thesis, we refer to these tasks as iterative decisions. This thesis applies the aforementioned innovative frameworks for decision making under uncertainty using contextual information in iterative decision making tasks faced daily by electricity market agents.Decision making is critical for any business to survive in a market environment. Examples of decision making tasks are inventory management, resource allocation or portfolio selection. Optimization, understood as the scientific discipline that studies how to solve mathematical programming problems, can help make more efficient decisions in many of these situations. Particularly relevant, because of their frequency and difficulty, are those decisions affected by uncertainty, i.e., in which some of the parameters that precisely determine the optimization problem are unknown when the decision must be made. Fortunately, the development of information technologies has led to an explosion in the availability of data that can be used to assist decisions affected by uncertainty. However, most of the available historical data do not correspond to the unknown parameter of the problem but originate from other related sources. This subset of data, potentially valuable for obtaining better decisions, is called contextual information. This thesis is framed within a new scientific effort that seeks to exploit the potential of data and, in particular, of contextual information in decision making. To this end, in this thesis, we have developed mathematical frameworks and data-driven optimization models that exploit contextual information to make better decisions in problems characterized by the presence of uncertain parameters

Journey decision making: the influence on drivers of dynamic information presented on variable message signs

In many highways environments electronic media such as variable message signs are increasingly being used to provide drivers with up-to-date dynamic information in order to influence driving decision making during journeys. These decisions may be associated with strategic choices, such as route selection, or tactical decisions, such as driving at a certain speed, or altering driving style. This paper presents a study that used two methods - a scenario approach and a medium-fidelity driving simulator. Data from both methods are presented here and include decision making and driving performance data. These data provide an insight into the role of information and other contextual influences in decision making in the driving context specifically, but also has useful implications for the way in which information should be designed in other decision making contexts, such as travel using public transport, or supporting real-time complex control operations. The use of multiple data collection approaches also enabled data comparisons to be made, thus improving overall confidence in conclusions. The paper highlights the role of familiarity with information wording and context, level of detail, interpreted meaning, previous experience and contextual cues on trust in information and consequently behaviour in response to the information presented

Data Innovation for International Development: An overview of natural language processing for qualitative data analysis

Availability, collection and access to quantitative data, as well as its limitations, often make qualitative data the resource upon which development programs heavily rely. Both traditional interview data and social media analysis can provide rich contextual information and are essential for research, appraisal, monitoring and evaluation. These data may be difficult to process and analyze both systematically and at scale. This, in turn, limits the ability of timely data driven decision-making which is essential in fast evolving complex social systems. In this paper, we discuss the potential of using natural language processing to systematize analysis of qualitative data, and to inform quick decision-making in the development context. We illustrate this with interview data generated in a format of micro-narratives for the UNDP Fragments of Impact project

Right for the Right Reason: Training Agnostic Networks

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

Public Participation in Environmental Planning in the Great Lakes Region

The need for greater public involvement in environmental decision-making has been highlighted in recent high-profile research reports and emphasized by leaders at all levels of government. In some cases, agencies have opened the door to greater participation in their programs. However, there is relatively little information on what can be gained from greater public involvement and what makes some programs work while others fail. This paper addresses these questions through an evaluation of public participation in environmental planning efforts in the Great Lakes region. The success of participation is measured using five criteria: educating participants, improving the substantive quality of decisions, incorporating public values into decision-making, reducing conflict, and building trust. The paper then discusses the relationship between success and a number of contextual and procedural attributes of a variety of cases. Data come from a "case survey," in which the authors systematically extract information from previously published studies of 30 individual participation cases. The authors conclude that public participation can accomplish important societal goals and that success depends, in large part, on the actions and commitment of government agencies.

Context-Aware Recursive Bayesian Graph Traversal in BCIs

Noninvasive brain computer interfaces (BCI), and more specifically Electroencephalography (EEG) based systems for intent detection need to compensate for the low signal to noise ratio of EEG signals. In many applications, the temporal dependency information from consecutive decisions and contextual data can be used to provide a prior probability for the upcoming decision. In this study we proposed two probabilistic graphical models (PGMs), using context information and previously observed EEG evidences to estimate a probability distribution over the decision space in graph based decision-making mechanism. In this approach, user moves a pointer to the desired vertex in the graph in which each vertex represents an action. To select a vertex, a Select command, or a proposed probabilistic Selection criterion (PSC) can be used to automatically detect the user intended vertex. Performance of different PGMs and Selection criteria combinations are compared over a keyboard based on a graph layout. Based on the simulation results, probabilistic Selection criterion along with the probabilistic graphical model provides the highest performance boost for individuals with pour calibration performance and achieving the same performance for individuals with high calibration performance.Comment: This work has been submitted to EMBC 201