Search Results Heading

MBRLSearchResults

mbrl.module.common.modules.added.book.to.shelf
Title added to your shelf!
View what I already have on My Shelf.
Oops! Something went wrong.
Oops! Something went wrong.
While trying to add the title to your shelf something went wrong :( Kindly try again later!
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
    Done
    Filters
    Reset
  • Discipline
      Discipline
      Clear All
      Discipline
  • Is Peer Reviewed
      Is Peer Reviewed
      Clear All
      Is Peer Reviewed
  • Series Title
      Series Title
      Clear All
      Series Title
  • Reading Level
      Reading Level
      Clear All
      Reading Level
  • Year
      Year
      Clear All
      From:
      -
      To:
  • More Filters
      More Filters
      Clear All
      More Filters
      Content Type
    • Item Type
    • Is Full-Text Available
    • Subject
    • Country Of Publication
    • Publisher
    • Source
    • Target Audience
    • Donor
    • Language
    • Place of Publication
    • Contributors
    • Location
4,008 result(s) for "Warren, William"
Sort by:
Can China lead? : reaching the limits of power and growth
\"A book for anyone doing business in China Most literature on doing business in emerging markets has focused on why to enter these markets and how to build your business once you get there. But with the rapid changes that globalization has brought on, what's needed is an updated look at the current difficulties of doing business in these regions-and in China in particular. Why is it so much harder for companies to operate there today even from just a decade ago? Three of the field's foremost experts, all Harvard Business School professors, explain the rapidly changing context and challenges of the region. Based on their combined experience, F. Warren McFarlan, William Kirby, and Regina Abrami argue that China is at an inflection point, with changes in its economic path that will play out in the coming decades. Dismantling persistent myths, the authors describe the rapidly changing context in China and the new challenges shaping business there, and examine whether companies should rethink their growth aspirations and strategies in the region. The book draws from more than 30 case studies by the authors on Chinese firms and other companies doing business there. A provocative and necessary addition to the global conversation, Can China Lead offers a radical reassessment of China's capabilities that flies in the face of conventional wisdom\"-- Provided by publisher.
Collective Motion in Human Crowds
The balletic motion of bird flocks, fish schools, and human crowds is believed to emerge from local interactions between individuals in a process of self-organization. The key to explaining such collective behavior thus lies in understanding these local interactions. After decades of theoretical modeling, experiments using virtual crowds and analysis of real crowd data are enabling us to decipher the “rules of engagement” governing these interactions. On the basis of such results, my students and I built a dynamical model of how a pedestrian aligns his or her motion with that of a neighbor and how these binary interactions are combined within a neighborhood of interaction. Computer simulations of the model generate coherent motion at the global level and reproduce individual trajectories at the local level. This approach has yielded the first experiment-driven, bottom-up model of collective motion, providing a basis for understanding more complex patterns of crowd behavior in both everyday and emergency situations.
Wolf story
A young father tells his five-year-old son humorous variations on the theme of a hen escaping the clutches of a wily wolf.
How You Get There From Here: Interaction of Visual Landmarks and Path Integration in Human Navigation
How do people combine their sense of direction with their use of visual landmarks during navigation? Cue-integration theory predicts that such cues will be optimally integrated to reduce variability, whereas cue-competition theory predicts that one cue will dominate the response direction. We tested these theories by measuring both accuracy and variability in a homing task while manipulating information about path integration and visual landmarks. We found that the two cues were near-optimally integrated to reduce variability, even when landmarks were shifted up to 90°. Yet the homing direction was dominated by a single cue, which switched from landmarks to path integration when landmark shifts were greater than 90°. These findings suggest that cue integration and cue competition govern different aspects of the homing response: Cues are integrated to reduce response variability but compete to determine the response direction. The results are remarkably similar to data on animal navigation, which implies that visual landmarks reset the orientation, but not the precision, of the path-integration system.
From Cognitive Maps to Cognitive Graphs
We investigate the structure of spatial knowledge that spontaneously develops during free exploration of a novel environment. We present evidence that this structure is similar to a labeled graph: a network of topological connections between places, labeled with local metric information. In contrast to route knowledge, we find that the most frequent routes and detours to target locations had not been traveled during learning. Contrary to purely topological knowledge, participants typically traveled the shortest metric distance to a target, rather than topologically equivalent but longer paths. The results are consistent with the proposal that people learn a labeled graph of their environment.
Information Is Where You Find It: Perception as an Ecologically Well-Posed Problem
Texts on visual perception typically begin with the following premise: Vision is an ill-posed problem, and perception is underdetermined by the available information. If this were really the case, however, it is hard to see how vision could ever get off the ground. James Gibson’s signal contribution was his hypothesis that for every perceivable property of the environment, however subtle, there must be a higher order variable of information, however complex, that specifies it—if only we are clever enough to find them. Such variables are informative about behaviorally relevant properties within the physical and ecological constraints of a species’ niche. Sensory ecology is replete with instructive examples, including weakly electric fish, the narwal’s tusk, and insect flight control. In particular, I elaborate the case of passing through gaps. Optic flow is sufficient to control locomotion around obstacles and through openings. The affordances of the environment, such as gap passability, are specified by action-scaled information. Logically ill-posed problems may thus, on closer inspection, be ecologically well-posed.
Local interactions underlying collective motion in human crowds
It is commonly believed that global patterns of motion in flocks, schools and crowds emerge from local interactions between individuals, through a process of self-organization. The key to explaining such collective behaviour thus lies in deciphering these local interactions. We take an experiment-driven approach to modelling collective motion in human crowds. Previously, we observed that a pedestrian aligns their velocity vector (speed and heading direction) with that of a neighbour. Here we investigate the neighbourhood of interaction in a crowd: which neighbours influence a pedestrian's behaviour, how this depends on neighbour position, and how the influences of multiple neighbours are combined. In three experiments, a participant walked in a virtual crowd whose speed and heading were manipulated. We find that neighbour influence is linearly combined and decreases with distance, but not with lateral position (eccentricity). We model the neighbourhood as (i) a circularly symmetric region with (ii) a weighted average of neighbours, (iii) a uni-directional influence, and (iv) weights that decay exponentially to zero by 5 m. The model reproduces the experimental data and predicts individual trajectories in observational data on a human ‘swarm’. The results yield the first bottom-up model of collective crowd motion.