Rethinking the sharing economy:The nature and organization of sharing in the 2015 refugee crisis

Our paper focuses on a non-standard sharing example that harbors the potential to disrupt received wisdom on the sharing economy. While originally entering the field to analyze, broadly from a governance perspective, how the 2015 refugee crisis was handled in Vienna, Austria, we found that the non-governmental organization Train of Hope - labeled as a "citizen start-up" by City of Vienna officials - played an outstanding role in mastering the crisis. In a blog post during his visit in Vienna at the time, and experiencing the refugee crisis first-hand, it was actually Henry Mintzberg who suggested reading the phenomenon as part of the "sharing economy". Continuing this innovative line of thought, we argue that our unusual case is in fact an excellent opportunity to discover important aspects about both the nature and organization of sharing. First, we uncover an additional dimension of sharing beyond the material sharing of resources (i.e., the economic dimension): the sharing of a distinct concern (i.e., the moral dimension of sharing). Our discovery exemplifies such a moral dimension that is rather different from the status quo materialistic treatments focusing on economic transactions and property rights arguments. Second, we hold that a particular form of organizing facilitates the sharing economy: the sharing economy organization. This particular organizational form is distinctive - at the same time selectively borrowing and skillfully combining features from platform organizations (e.g., use of technology as an intermediary for exchange and effective coordination, ability to tap into external resources) and social movements (e.g., mobilization, shared identity, collective action). It is a key quality of this form of organization to enable the balancing of the two dimensions inherent in the nature of sharing: economic and moral. Our paper contributes to this Special Issue of the Academy of Management Discoveries by highlighting and explaining the two-fold economic and moral nature of sharing and the organization of sharing between movement and platform

Hybrid coordination of city organisations: The rule of people and culture in the shadow of structures

Under far-reaching reforms, many cities have delegated core tasks previously delivered by their administrations to independent organisations that they formally own, e.g. municipal companies,or supervise, e.g. municipal trust funds. The coordination of these (as we call them) ‘domestic’city organisations has proven challenging. Extant literature argues that such coordination isachieved through a mix of various hierarchical, market and network mechanisms. Yet it is unclearhow these modes are combined. Addressing this gap, we ask: How do governance modes interactin the hybrid coordination of domestic city organisations? Analysing the case of Vienna, where 100 domestic organisations employ about 60,000 people, we find that while cultural mechanisms, rooted in the network mode, are predominant, they unfold in the shadow of latent structural mechanisms, which are associated with hierarchy and market. In the background, structural mechanisms keep cultural coordination effective, while cultural mechanisms allow structural coordination to remain (generally) hidden. This study aims to contribute to the literature on the governance of public organisations by exploring the relationship between governance modes as well as furthering urban governance studies by applying insights from studies on the coordination of public organisations to the city context

Coherent Bayesian inference on compact binary inspirals using a network of interferometric gravitational wave detectors

Presented in this paper is a Markov chain Monte Carlo (MCMC) routine for conducting coherent parameter estimation for interferometric gravitational wave observations of an inspiral of binary compact objects using data from multiple detectors. The MCMC technique uses data from several interferometers and infers all nine of the parameters (ignoring spin) associated with the binary system, including the distance to the source, the masses, and the location on the sky. The Metropolis-algorithm utilises advanced MCMC techniques, such as importance resampling and parallel tempering. The data is compared with time-domain inspiral templates that are 2.5 post-Newtonian (PN) in phase and 2.0 PN in amplitude. Our routine could be implemented as part of an inspiral detection pipeline for a world wide network of detectors. Examples are given for simulated signals and data as seen by the LIGO and Virgo detectors operating at their design sensitivity.Comment: 10 pages, 4 figure

Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data

We present a Bayesian approach to the problem of determining parameters for coalescing binary systems observed with laser interferometric detectors. By applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs sampler, we demonstrate the potential that MCMC techniques may hold for the computation of posterior distributions of parameters of the binary system that created the gravity radiation signal. We describe the use of the Gibbs sampler method, and present examples whereby signals are detected and analyzed from within noisy data.Comment: 21 pages, 10 figure

Bayesian inference on compact binary inspiral gravitational radiation signals in interferometric data

Presented is a description of a Markov chain Monte Carlo (MCMC) parameter estimation routine for use with interferometric gravitational radiational data in searches for binary neutron star inspiral signals. Five parameters associated with the inspiral can be estimated, and summary statistics are produced. Advanced MCMC methods were implemented, including importance resampling and prior distributions based on detection probability, in order to increase the efficiency of the code. An example is presented from an application using realistic, albeit fictitious, data.Comment: submitted to Classical and Quantum Gravity. 14 pages, 5 figure

Inference on inspiral signals using LISA MLDC data

In this paper we describe a Bayesian inference framework for analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Challenge 1.2 (MLDC), and implemented a Markov chain Monte Carlo (MCMC) algorithm to facilitate exploration and integration of the posterior distribution over the 9-dimensional parameter space. Here we present intermediate results showing how, using this method, information about the 9 parameters can be extracted from the data.Comment: Accepted for publication in Classical and Quantum Gravity, GWDAW-11 special issu

Coherent Bayesian analysis of inspiral signals

We present in this paper a Bayesian parameter estimation method for the analysis of interferometric gravitational wave observations of an inspiral of binary compact objects using data recorded simultaneously by a network of several interferometers at different sites. We consider neutron star or black hole inspirals that are modeled to 3.5 post-Newtonian (PN) order in phase and 2.5 PN in amplitude. Inference is facilitated using Markov chain Monte Carlo methods that are adapted in order to efficiently explore the particular parameter space. Examples are shown to illustrate how and what information about the different parameters can be derived from the data. This study uses simulated signals and data with noise characteristics that are assumed to be defined by the LIGO and Virgo detectors operating at their design sensitivities. Nine parameters are estimated, including those associated with the binary system, plus its location on the sky. We explain how this technique will be part of a detection pipeline for binary systems of compact objects with masses up to 20 \sunmass, including cases where the ratio of the individual masses can be extreme.Comment: Accepted for publication in Classical and Quantum Gravity, Special issue for GWDAW-1

II: Bayesian Methods for Cosmological Parameter Estimation from Cosmic Microwave Background Measurements

We present a strategy for a statistically rigorous Bayesian approach to the problem of determining cosmological parameters from the results of observations of anisotropies in the cosmic microwave background. Our strategy relies on Markov chain Monte Carlo methods, specifically the Metropolis-Hastings algorithm, to perform the necessary high-dimensional integrals. We describe the Metropolis-Hastings algorithm in detail and discuss the results of our test on simulated data.Comment: This paper expands on the work presented in astro-ph/0006401, and now includes a test of the method. 15 pages, 1 figur

The logic of tact:How decisions happen in situations of crisis

The mass-migration of refugees in the fall 2015 posed an immense humanitarian and logistical challenge: exhausted from their week-long journeys, refugees arrived in Vienna in need of care, shelter, food, medical aid, and onward transport. The refugee crisis was managed by an emerging polycentric and inter-sectoral collective of organizations. In this paper, we investigate how, during such a situation, leaders of these organizations made decisions in concert with each other and hence sustained the collective's capacity to act collectively. We ask: what was the logic of decision-making that orchestrated collective action during the crisis? In answering this question, we make the following contribution: departing from March's logics of consequences and appropriateness as well as Weick's work on sensemaking during crisis, we introduce an alternative logic that informed decision-making: the logic of tact. With this concept we (a) offer a better understanding of how managers make decisions under the condition of bounded rationality and the simultaneous transgression of their institutional identity in situations of crisis; and we (b) show that in decision-making under duress cognition is neither ahead of action, nor is action ahead of cognition; rather, tact explicates the rapid switching between cognition and action, orchestrating decision-making through this interplay