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How to develop exceptional digital experiences at velocity
I love this => Emphasizes impact of front and back stage on #customer #experience + How to build your #service #design #consulting practice by scoping projects in front stage before tackling back stage complexity + great #airline example #CX
service  design  consulting  front  back  stage  blueprint  journey  customer  experience  map  digital  application  coordination  theater  example  disney  airline  strategy  complexity  howto  privacy  tablet  flight  attendant  csr19 
10 days ago by csrollyson
CityMetric -- “Birmingham isn’t a big city at peak times”: How poor public transport explains the UK’s productivity puzzle
'...While the tram is substantially quicker at all times than the bus, the reliability of its timing, even during the most congested periods, provides an additional large benefit to users. -- We think that people generate the most agglomeration benefits for a city when they travel at peak times, to get to and from work, meetings, and social events. Our tool shows us that, at the times when people need to travel in order to generate these benefits, buses are extremely slow. And since buses are by far the largest mode of public transport in Birmingham, this is likely to have significantly higher impact there than in Lyon; in the latter, the largest mode of public transport is the metro, which delivers reliable journey times no matter the time of day. -- Our hypothesis is that Birmingham’s reliance on buses makes its effective population much smaller than its real population. This reduces its productivity by sacrificing agglomeration benefits. -- ... Our hypothesis is that, by relying on buses that get caught in congestion at peak times for public transport, Birmingham sacrifices significant size and thus agglomeration benefits to cities like Lyon, which rely on trams and metros. This is based on our calculations that a whole-city tramway system for Birmingham would deliver an effective size roughly equal to the OECD-defined population. -- This difference seems to explain a significant proportion of the productivity gap between UK large cities and their European equivalents.'
economics  cities  transport  coordination  productivity 
9 weeks ago by adamcrowe
Markets Are Eating The World
Stretching to make blockchain a crucial technology
economics  Technology  History  adoption  coordination 
10 weeks ago by amaah
How Integration Is Actually Working in Germany — Refugees Deeply
Concise review of Merkel's #refugee #integration initiatives + 4 areas for improvement #germany
refugee  host  germany  program  federal  state  government  coordination  review  2018  work  bank  results  19eyz  bullsi 
12 weeks ago by csrollyson
Keeping CALM: when distributed consistency is easy | the morning paper
Keeping CALM: when distributed consistency is easy Hellerstein & Alvaro, arXiv 2019 The CALM conjecture (and later theorem) was first introduced to the world in a 2010 keynote talk at PODS. Behind its simple formulation there’s a deep lesson to be learned with the power to create ripples through our industry akin to the influence…
adrian_colyer  software_architecture  distributed_systems  CALM  databases  coordination 
march 2019 by mreinbold
Learning to Coordinate: A Study in Retail Gasoline
"Byrne, David P., and Nicolas de Roos. 2019. "Learning to Coordinate: A Study in Retail Gasoline." American Economic Review, 109 (2): 591-619."
!paywalled  coordination  economics  !N-🏺-information-geisteswissenschaften 
february 2019 by beyondseven
[1811.00607] Exploring the Equivalence between Dynamic Dataflow Model and Gamma - General Abstract Model for Multiset mAnipulation
With the increase of the search for computational models where the expression of parallelism occurs naturally, some paradigms arise as options for the next generation of computers. In this context, dynamic Dataflow and Gamma - General Abstract Model for Multiset mAnipulation) - emerge as interesting computational models choices. In the dynamic Dataflow model, operations are performed as soon as their associated operators are available, without rely on a Program Counter to dictate the execution order of instructions. The Gamma paradigm is based on a parallel multiset rewriting scheme. It provides a non-deterministic execution model inspired by an abstract chemical machine metaphor, where operations are formulated as reactions that occur freely among matching elements belonging to the multiset. In this work, equivalence relations between the dynamic Dataflow and Gamma paradigms are exposed and explored, while methods to convert from Dataflow to Gamma paradigm and vice versa are provided. It is shown that vertices and edges of a dynamic Dataflow graph can correspond, respectively, to reactions and multiset elements in the Gamma paradigm. Implementation aspects of execution environments that could be mutually beneficial to both models are also discussed. This work provides the scientific community with the possibility of taking profit of both parallel programming models, contributing with a versatility component to researchers and developers. Finally, it is important to state that, to the best of our knowledge, the similarity relations between both dynamic Dataflow and Gamma models presented here have not been reported in any previous work.
concurrency  rather-interesting  to-understand  coordination  computer-science  ReQ 
february 2019 by Vaguery
[1810.09202] Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation
Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly dynamic, which makes it hard to learn abstract representations of their mutual interplay. In this paper, we propose graph convolutional reinforcement learning for multi-agent cooperation, where graph convolution adapts to the dynamics of the underlying graph of the multi-agent environment, and relation kernels capture the interplay between agents by their relation representations. Latent features produced by convolutional layers from gradually increased receptive fields are exploited to learn cooperation, and the cooperation is further boosted by temporal relation regularization for consistency. Empirically, we show that our method substantially outperforms existing methods in a variety of cooperative scenarios.
machine-learning  agent-based  coordination  rather-interesting  reinforcement-learning  teams  to-understand  rather-convoluted-(heh)  collective-behavior 
february 2019 by Vaguery
A.M.B.R.O.S.I.A: Providing Performant Virtual Resiliency for Distributed Applications
When writing today’s distributed programs, which frequently span both devices and cloud services, programmers are faced with complex decisions and coding tasks around coping with failure, especially when these distributed components are stateful. If their application can be cast as pure data processing, they benefit from the past 40-50 years of work from the database community, which has shown how declarative database systems can completely isolate the developer from the possibility of failure in a performant manner. Unfortunately, while there have been some attempts at bringing similar functionality into the more general distributed programming space, frequently called “exactly once execution”, a compelling general-purpose system must be performant, support a variety of machine types with varying resiliency goals, and be language agnostic, allowing distributed components written in different languages to communicate. This paper introduces the first system, Ambrosia, to satisfy all these requirements. We coin the term “virtual resiliency”, analogous to virtual memory, for the fundamental mechanisms (already present in data processing systems for decades) that allow programmers to write their applications in a failure oblivious way. Of interest to our community is the effective reapplication of much database performance optimization technology to make Ambrosia more performant than many of today’s non-resilient cloud solutions.
december 2018 by mpm

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