2,266 research outputs found
Synthesising Strategy Improvement and Recursive Algorithms for Solving 2.5 Player Parity Games
2.5 player parity games combine the challenges posed by 2.5 player
reachability games and the qualitative analysis of parity games. These two
types of problems are best approached with different types of algorithms:
strategy improvement algorithms for 2.5 player reachability games and recursive
algorithms for the qualitative analysis of parity games. We present a method
that - in contrast to existing techniques - tackles both aspects with the best
suited approach and works exclusively on the 2.5 player game itself. The
resulting technique is powerful enough to handle games with several million
states
Transient Reward Approximation for Continuous-Time Markov Chains
We are interested in the analysis of very large continuous-time Markov chains
(CTMCs) with many distinct rates. Such models arise naturally in the context of
reliability analysis, e.g., of computer network performability analysis, of
power grids, of computer virus vulnerability, and in the study of crowd
dynamics. We use abstraction techniques together with novel algorithms for the
computation of bounds on the expected final and accumulated rewards in
continuous-time Markov decision processes (CTMDPs). These ingredients are
combined in a partly symbolic and partly explicit (symblicit) analysis
approach. In particular, we circumvent the use of multi-terminal decision
diagrams, because the latter do not work well if facing a large number of
different rates. We demonstrate the practical applicability and efficiency of
the approach on two case studies.Comment: Accepted for publication in IEEE Transactions on Reliabilit
Lazy Probabilistic Model Checking without Determinisation
The bottleneck in the quantitative analysis of Markov chains and Markov
decision processes against specifications given in LTL or as some form of
nondeterministic B\"uchi automata is the inclusion of a determinisation step of
the automaton under consideration. In this paper, we show that full
determinisation can be avoided: subset and breakpoint constructions suffice. We
have implemented our approach---both explicit and symbolic versions---in a
prototype tool. Our experiments show that our prototype can compete with mature
tools like PRISM.Comment: 38 pages. Updated version for introducing the following changes: -
general improvement on paper presentation; - extension of the approach to
avoid full determinisation; - added proofs for such an extension; - added
case studies; - updated old case studies to reflect the added extensio
Symblicit Exploration and Elimination for Probabilistic Model Checking
Binary decision diagrams can compactly represent vast sets of states,
mitigating the state space explosion problem in model checking. Probabilistic
systems, however, require multi-terminal diagrams storing rational numbers.
They are inefficient for models with many distinct probabilities and for
iterative numeric algorithms like value iteration. In this paper, we present a
new "symblicit" approach to checking Markov chains and related probabilistic
models: We first generate a decision diagram that symbolically collects all
reachable states and their predecessors. We then concretise states one-by-one
into an explicit partial state space representation. Whenever all predecessors
of a state have been concretised, we eliminate it from the explicit state space
in a way that preserves all relevant probabilities and rewards. We thus keep
few explicit states in memory at any time. Experiments show that very large
models can be model-checked in this way with very low memory consumption
Corporate Governance and Value Creation: Evidence from Private Equity
We examine deal-level data on private equity transactions in the UK initiated during
the period 1996 to 2004 by mature private equity houses. We un-lever the deal-level equity return and adjust for (un-levered) return to quoted peers to extract a measure of "alpha" or abnormal performance of the deal. The alpha is significantly positive on average and robust during sector downturns. In the cross-section of deals, higher alpha is related to greater improvement in EBITDA to Sales ratio (margin) and greater growth in EBITDA multiple during the private phase, relative to that of quoted peers. In particular, deals with higher alpha either grow their margins more substantially, and/or grow multiples more substantially,
whilst expanding their revenues only in line with the sector. Based on interviews with general partners involved with the deals, we find that deals with higher alpha and higher margin growth are associated with greater intensity of engagement of private equity houses during the early phase of the deal, employment of value-creation initiatives for productivity and organic growth, and complementing top management with external support. Overall, our results are consistent with mature private equity houses creating value for portfolio companies through
active ownership and governance
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