Brains, Evolution, Computers & Companies
Correlative mappings of decision making and learning systems
by
Ed Lee
Objectives of talk
Demonstrate how some decision making and learning in companies correlates to similar processes in the brain
Present a useful tool for analyzing our own decision making and learning attitudes
Introductory remarks
Researching a book: Plantations in the Rain Forest: the future of civilization a study of human systems in the biosphere
Fundamental questions of book: How has nature generated robust life for 4
billion years? What can we do to increase civilization’s
robustness? Problem: author is an engineer, businessman
doing a cram course on nature
Strata of Evolution
Quanta, Uncertainty
Atoms
Inorganic Molecules
Organic Molecules
Cells
MulticellularOrganisms
Mammals
Evolution as seen by an engineer
Localized order (structure) emerges from global randomness Local negative entropy Extraction, construction processes
Strata with bi-directional stochastic coupling Catalysts and enzymes
Dynamic equilibriums Overproduction and vigorous pruning
(opposites) keep each other robust
Evolution extracted complex brains
Quanta,Uncertainty
RandomMotion
EnhancedMotion
ReactiveMotion
Hunting,gathering
Pro-ActiveCultivation,Civilization
DirectedMotion
Uncertainty is a key tool
Weak bonds, stochastic links, ensembles of crude cues Mutations Plasticity
Produces diverse, niche solutions Robust response for unforeseen global
changes Stabilizes dynamic systems
Friction in mechanical systems Trading costs, varied beliefs in stock markets
Ambivalent attitudes
Can be threatening Error, noise, turbulence, chaos, illness Hierarchies formed to control, eliminate them
Essential to playfulness Games Humour
Critical to communication
How old are you?
An incomplete question To nearest year culturally implicit
Uncertain answer enabled a quick response Decoupled most contexts Short coding of question and answer
Probably a maximum Value/effort Value/time
Other extreme: Descartes
Which attitude fits you?
Ready, fire, aim! Ready, aim, fire! Ready, aim, don’t fire…
unless certain of bulls eye! You made me miss!
Choice determines Time to respond to a stimulus
(sense of urgency) Probable accuracy of response Number of stimuli responded to Rate and nature of learning/change
Which choice fits evolution? Brains?
Attitudes of decision makers
Fearful
Accepts Responsibility
Disowns Responsibility
VictimUses past failures to excuse personalresponsibility, or to demand specialconsiderations
Change
StabilityInnovators
Early Adopters
CrooksTerrorists
Addicts
AdventurerUncertainty is spice of life.Success enables more risk
Cr af t sper sonSeeks success through excellenceUncertainty is rationally minimized
BureaucratAvo
ids
Failur
e
Wants no uncertainty. Avoids anyaccountability for failure, at all costs
Companies are organisms
Life Cycles Embryos (startups) Growth and specialization Maturity Senility and death
Metabolic requirements Profits measure input/output efficiencies
Can reproduce (sexual, asexual, cloning)
Living community (flesh and structure) People (employees, customers, investors, etc.) Methods (maps, procedures, norms, policies) Materials (money, equipment, facilities, products)
Companies exercise brain-like functions Mildly intelligent Learn, remember
Experiences Simple to moderately complex algorithms Store in locally meaningful maps
Layers of decision making elements Complexity, cycle times, number of similar
decisions per year Differing cue sets
Typical Organization Chart
CEO
COO CIOCFO
VPEngineering
VPManujfacturing
VPMarketing
Finance
Purchasing
Assy & Test
Mfg.Engineering
Mgr. HumanResourcesa
Marketing
National Sales
InternationalSales
ProjectManager
ProjectManager
IT
Documentation
Actually Stratified
CEO
COO CIOCFO
VPEngineering
VPManujfacturing
VPMarketing
Finance
Purchasing
Assy & Test
Mfg.Engineering
Mgr. HumanResourcesa
Marketing
National Sales
InternationalSales
ProjectManager
ProjectManager
IT
Documentation
Time scales of decisions for strata
Short term: decisions from experience, internal processing, predictable results
Long term: observe competitors choices, uncertain results
10
101 102 103 104 105 106 107 108
100
1000
10000
1 HR 30 D 1 YR
CEO
DepartmentManagers
Workers
Strategic CEO Functions
Select key executives Sponsor them
Lead executive team Maintain cohesive/timely strategic vision Maintain flexibility in changing market Set tone, spirit by example Resolve intrinsic strategic conflicts
Select, lead key stakeholders Board
(Results of efforts affect “health” 2-3 yrs later)
Distribution of decision making attitudes
Young, small companies Adventurer at top Craftspeople dominate lower strata Quickly adapt to market
Old, large companies Craftsperson or Bureaucrat at top Bureaucrats dominate middle management Hierarchical processes Expect market to adapt to them
Dorsal view of company
Engineering
Materials
Finance
Admin and IT
HumanResources
Sales and Marketing
CEOCFO COO
Conf.Room
Lobby
Manufacturing
ManufacturingEngineering
Anterior
Posterior
Learning and Memory
Local maps specialized by function Working memory: people Short term: notes, redlines Long term: formal documents, data bases
Some long term information received from other areas within company converted to local maps
Thermal maps
Engineering
Materials
Finance
Admin and IT
HumanResources
Sales and Marketing
CEOCFO COO
Conf.Room
Lobby
Manufacturing
ManufacturingEngineering
Engineering
Materials
Finance
Admin and IT
HumanResources
Sales and Marketing
CEOCFO COO
Conf.Room
Lobby
Manufacturing
ManufacturingEngineering
Launch new design project Launch new product
Marketing Funnel: extracting customers from environment
UnwashedMasses
Suspects ProspectsProspects
in-heatCustomers
Cue Set 1 Cue Set 2 Cue Set 3 Cue Set 4
Recruit
Computers: artifacts of Reason
Artifacts of 19th century belief in a clockwork universe Rigid hierarchical control Synchronous Centralized decision making Deterministic
Characteristics
Finite states Deterministic, pig-headed
make mistakes, but never learn Fast, ~109 ops/s
~2 x106 ops during an action potential Extremely complex algorithms Fragile, tolerates
<10-17 bit errors/ s No connection errors
Computer’s hierarchical Architecture
Inputs(Observe)
Electronic Bus(Communicate)
7 56
121110
8 4
21
9 3
Clock
System Timing
Outputs(Act)
CPU(Decide)
Memory(Store)
Instr.Set Map
OperatingSystem Bios
Programs andData
Architecture
CPU rich in logic, only element capable of reading maps, implementing algorithms
CPU controls all timing and relationships…the ultimate micro-manager
Memory stores patterns that have no intrinsic meaning. Only meaningful to CPU provided it keeps track of
storage locations relative to program String of patterns, one degree of associative freedom
Bottleneck is in transporting codes in and out of the CPU (von Neumann Bottleneck)
Thermal maps don’t change for novel or familiar tasks
Some conclusions Companies have some useful correlations to Brains
extract and process the familiar ignore or adapt to the unfamiliar
selectively learn stratified, stochastic
bi-directional influences time scales and complexities
Computers don’t correlate with brains…but do correlate with some rational beliefs process the specified ignore or crash from the unspecified
- never learn deterministic hierarchy
bottom-up data top-down control
Thanks
To Susumu Tonegawa and Bob Silvey for the opportunity to be here
To Matt Wilson and Morgan Sheng for helpful feedback
To Jeffrey Goodman for his repeated help and some great laughs.