On optimal decision-making in
brains and social insect colonies
Marshall, Bogacz, Dornhaus, Planque,
Kovacs,Franks
Presented by
Reffat Sharmeen, Kolli Sai Namratha
Contents
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Introduction
Optimal decision-making
Decision making in cortex
Usher-McClelland model
Decision making in social insect colonies
Models of house-hunting
Discussion
Introduction
• Animals constantly invest time and energy to make
decisions.
• Need to compromise between speed and accuracy.
• Decision making model in primate brain is compared to
house hunting models by social insect colonies.
• Striking parallels are evident between decision-making in
primate brains and collective decision-making in social
insect colonies.
Optimal decision-making
• Uncertain information are processed to choose among
alternatives.
• Example : Follow a display filled with moving dots.
• Decision making process can be represented as
Brownian motion on a line moving towards the correct
hypothesis, known as diffusion model.
• Sequential probability ratio test(SPRT) gathers evidence
for two hypothesis until likelihood ratio reaches a positive
or negative threshold.
Diffusion Model
Decision making in cortex
• Neurons in medial Temporal area (MT)are responsible to
process the motions in visual field.
• Neurons in lateral intraparietal area(LIP) and frontal eye
field control eye movement.
• Over time LIP neurons integrate input from MT neurons
and accumulate sensory evidence.
• When LIP neuron’s activity is over a threshold, the
decision is made and eye is moved to the corresponding
direction.
Usher-McClelland model
• A decision making model in primate brains.
• Each neural population receives noisy input signal and
inhibits activation of the other to a degree proportional to
its own activation.
• These populations leak incoming evidence.
• If activity of either of the populations reaches a
threshold, the decision is made.
• Based on parameters Usher McClelland model
approximates optimal decision making.
Usher-McClelland model
Decision making in social insect
colonies
• Honeybee and ant colonies hunt for new nest sites.
• Trade off between emigration duration and information
about potential nest sites.
• Ant scouts discover site, recruit nest mates who teach
others the route, thus making a collective decision based
on positive feedback.
• Bee scouts discover sites, recruit others for positive
feedback and switch to the new site after the decision
has been made.
• No central control, individuals use only local information.
House-hunting in T.albipennis
• Ants switch directly from uncommitted to committed state
by discovering site and becoming recruiters for the new
site.
• Recruiters for a site can switch to recruit for other site or
switch to being uncommitted to any site.
• Decision will be optimal if individuals have global
knowledge about the alternatives available, which makes
this model biologically unrealistic.
House hunting with indirect
switching
• Committed scouts should be completely uncommitted to
change their commitment.
• Uncommitted scouts can spontaneously discover
alternative sites at a rate which is independent of site
quality.
• This model cannot be reduced to two independent
random processes. Also it does not asymptotically
converge to the diffusion model, so it cannot be a
statistically optimal decision making strategy .
House hunting with direct switching
• Scouts can directly switch their commitment between
alternative sites.
• During the emigration process honeybees enter in
decision making phase and number of alternative sites
are reduced.
• Thus a close, inferior, easy to find site may be chosen
due to positive feedback.
• When all scouts are committed in the colony, decision is
optimal. Without decay this model can be described as
asymptotically optimal.
Direct switching model
Discussion
• First optimality hypothesis for collective decision making
during emigration for social insect colonies.
• Formally investigated similarities between neural
decision making process and collective decision making
in social insect colonies.
• Direct switching model approximates statistically optimal
decision making.
• If direct switching does not occur their hypothesis can at
least theoretically quantify the cost of deviation from
optimality.
Discussion
• Only binary decision case was considered. In real world
optimal decision making is harder for more than two
alternatives.
• Information about all the alternatives is not available
from the beginning, discovery of best available
alternative may be quite late.
• Bandit problem- should scouts evaluate existing
alternatives or discover unknown alternatives.