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Classes of decision analysis
The ultimate task of an engineer consists of developing a consistent decision procedure for the
planning, design, construction and use and management of a project. Moreover, the utility over the
entire lifetime of the project should be maximized, considering requirements with respect to safety
of individuals and the environment as specified in regulations. Due to the fact that the information
with respect to design parameters is usually incomplete or uncertain, decisions are made under
uncertainty. In order to cope with this, Bayesian statistical decision theory can be used to incorporate
objective as well as subjective information (e.g. engineering judgement). In this factsheet, the
decision tree is presented and answers are given for questions on how new data can be combined
with prior probabilities that have been assigned, and whether it is beneficial or not to collect more
information before the final decision is made. Decision making based on prior analysis and posterior
analysis is briefly explained. Pre-posterior analysis is considered in more detail and the Value of
Information (VoI) is defined
Spatio-temporal Edge Service Placement: A Bandit Learning Approach
Shared edge computing platforms deployed at the radio access network are
expected to significantly improve quality of service delivered by Application
Service Providers (ASPs) in a flexible and economic way. However, placing edge
service in every possible edge site by an ASP is practically infeasible due to
the ASP's prohibitive budget requirement. In this paper, we investigate the
edge service placement problem of an ASP under a limited budget, where the ASP
dynamically rents computing/storage resources in edge sites to host its
applications in close proximity to end users. Since the benefit of placing edge
service in a specific site is usually unknown to the ASP a priori, optimal
placement decisions must be made while learning this benefit. We pose this
problem as a novel combinatorial contextual bandit learning problem. It is
"combinatorial" because only a limited number of edge sites can be rented to
provide the edge service given the ASP's budget. It is "contextual" because we
utilize user context information to enable finer-grained learning and decision
making. To solve this problem and optimize the edge computing performance, we
propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore,
SEEN is extended to scenarios with overlapping service coverage by
incorporating a disjunctively constrained knapsack problem. In both cases, we
prove that our algorithm achieves a sublinear regret bound when it is compared
to an oracle algorithm that knows the exact benefit information. Simulations
are carried out on a real-world dataset, whose results show that SEEN
significantly outperforms benchmark solutions
von Neumann-Morgenstern and Savage Theorems for Causal Decision Making
Causal thinking and decision making under uncertainty are fundamental aspects
of intelligent reasoning. Decision making under uncertainty has been well
studied when information is considered at the associative (probabilistic)
level. The classical Theorems of von Neumann-Morgenstern and Savage provide a
formal criterion for rational choice using purely associative information.
Causal inference often yields uncertainty about the exact causal structure, so
we consider what kinds of decisions are possible in those conditions. In this
work, we consider decision problems in which available actions and consequences
are causally connected. After recalling a previous causal decision making
result, which relies on a known causal model, we consider the case in which the
causal mechanism that controls some environment is unknown to a rational
decision maker. In this setting we state and prove a causal version of Savage's
Theorem, which we then use to develop a notion of causal games with its
respective causal Nash equilibrium. These results highlight the importance of
causal models in decision making and the variety of potential applications.Comment: Submitted to Journal of Causal Inferenc
Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learning
Shared edge computing platforms, which enable Application Service Providers
(ASPs) to deploy applications in close proximity to mobile users are providing
ultra-low latency and location-awareness to a rich portfolio of services.
Though ubiquitous edge service provisioning, i.e., deploying the application at
all possible edge sites, is always preferable, it is impractical due to often
limited operational budget of ASPs. In this case, an ASP has to cautiously
decide where to deploy the edge service and how much budget it is willing to
use. A central issue here is that the service demand received by each edge
site, which is the key factor of deploying benefit, is unknown to ASPs a
priori. What's more complicated is that this demand pattern varies temporally
and spatially across geographically distributed edge sites. In this paper, we
investigate an edge resource rental problem where the ASP learns service demand
patterns for individual edge sites while renting computation resource at these
sites to host its applications for edge service provisioning. An online
algorithm, called Context-aware Online Edge Resource Rental (COERR), is
proposed based on the framework of Contextual Combinatorial Multi-armed Bandit
(CC-MAB). COERR observes side-information (context) to learn the demand
patterns of edge sites and decides rental decisions (including where to rent
the computation resource and how much to rent) to maximize ASP's utility given
a limited budget. COERR provides a provable performance achieving sublinear
regret compared to an Oracle algorithm that knows exactly the expected service
demand of edge sites. Experiments are carried out on a real-world dataset and
the results show that COERR significantly outperforms other benchmarks
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Elicitation of ambiguous beliefs with mixing bets
I consider the elicitation of ambiguous beliefs about an event and show how
to identify the interval of relevant probabilities (representing ambiguity
perception) for several classes of ambiguity averse preferences. The agent
reveals her preference for mixing binarized bets on the uncertain event and its
complement under varying betting odds. Under ambiguity aversion, mixing is
informative about the interval of beliefs. In particular, the mechanism allows
to distinguish ambiguous beliefs from point beliefs, and identifies the belief
interval for maxmin preferences. For ambiguity averse smooth second order and
variational preferences, the mechanism reveals inner bounds for the belief
interval, which are sharp under additional assumptions. In an experimental
study, participants perceive almost as much ambiguity for natural events
(generated by the stock exchange and by a prisoners dilemma game) as for the
Ellsberg Urn, indicating that ambiguity may play a role in real-world decision
making
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