Lamb, Warren

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  • Personality
| System number 987007436612705171

Information for Authority record

Name (Latin)
Lamb, Warren
Date of birth
1923-04-28
Date of death
2014-01-21
Field of activity
Movement, Psychology of
Occupation
Business consultants
Associated Language
eng
Gender
male
MARC
MARC

Other Identifiers

VIAF: 60644263
Wikidata: Q7970336
Library of congress: n 88202696
HAI10: 000724144
Sources of Information
  • A framework for understanding movement, 2012:t.p. (Warren Lamb) p. 1 (b. 1923 in Wallasey, Merseyside)
  • Independent (online), viewed Feb. 6, 2014(Warren Lamb; b. Apr. 28, 1923, Wallasey; d. Jan. 21, 2014, Claremont, Calif.; pioneer of movement analysis whose behavioural predictions were sought by industries and governments)
  • His Body code, 1987, c1979:t.p. (Warren Lamb)
  • LC data base, 5-10-89(hdg.: Lamb, Warren)
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Wikipedia description:

Warren Lamb (28 April 1923 – 21 January 2014) was a British management consultant and pioneer in the field of nonverbal behavior. After studying with Rudolf Laban he developed Movement Pattern Analysis - a system for analysing and interpreting movement behaviour, which has been applied in numerous fields including management consulting, executive recruitment and therapy. Lamb used the MPA system in advising multinational corporations, typically at top team level, and also government organizations. Lamb differentiated his system from the popular body language literature and argued that the key to interpreting behaviour was not fixed gestures but the dynamics of movement. Lamb's underlying theory was that each individual has a unique way of moving which is constant and that these distinct movement patterns reflect (and predict) the individual's way of thinking and behaving. In MPA he developed a system for identifying these patterns and relating them to behaviours, with the aim of predicting how people will behave in various situations based on their movement patterns. Recent studies led by Harvard University and Brown University in the United States reported significant predictive reliability for the system.

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