years-simulated [stop]
ask turtles [move]
ask turtles [do-accounting]
output
color-turtles
color-patches
draw-links
end
to move
move-to best-patch
end
to-report best-patch
ifelse objective = "Optimize"
[
report best-patch-utility
]
[
report best-patch-satisfice
]
end
to-report target-patches
if vision-mode = "neighbors" [ report target-patches-neighbors ]
if vision-mode = "radius" [ report target-patches-radius ]
if vision-mode = "links" [ report target-patches-links ]
if vision-mode = "links + radius" [ report target-patches-links-radius ]
end
to-report target-patches-neighbors
let targets neighbors with [ not any? turtles-here ]
set targets (patch-set targets patch-here)
report targets
end
to-report target-patches-radius
let targets patches in-radius sense-radius with [ not any? turtles-here ]
set targets (patch-set targets patch-here)
report targets
end
to-report target-patches-links
let targets target-patches-neighbors
set targets (patch-set
targets
([neighbors with [not any? turtles-here]] of out-link-neighbors)
)
report targets
end
to-report target-patches-links-radius
let targets target-patches-radius
set targets (patch-set
targets
([patches in-radius sense-radius with [not any? turtles-here]] of out-link-neighbors)
)
report targets
end
to-report best-patch-utility
let potential-destinations target-patches
; Identify the best one of the destinations
report max-one-of potential-destinations [utility-for myself]
end
to-report best-patch-satisfice
if current-utility < income-threshold
[
let potential-destinations other target-patches
if any? potential-destinations
[ report one-of potential-destinations ]
]
report patch-here
end
to do-accounting
; First, add this year's profits
set wealth (wealth + profit)
; Now see if the investment failed
if (wealth < 0) or (random-float 1.0 < annual-risk)
[
set business-failures business-failures + 1
set wealth 0
]
; For output, update the utility of the investor
set current-utility utility-for self
end
to-report utility-for [a-turtle]
let turtles-wealth 0
ifelse a-turtle = nobody
[
; if no turtle is selected, report the utility for the wealth of the average turtle
set turtles-wealth g-mean-wealth
]
[
; For the simple microeconomic utility function, first calc. expected
; investor wealth over the time horizon
set turtles-wealth [wealth] of a-turtle
]
let utility turtles-wealth + (profit * decision-time-horizon)
; Then factor in risk of failure over time horizon
set utility utility * ((1 - annual-risk) ^ decision-time-horizon)
if utility < 0 [ set utility 0 ]
report utility
end
to color-patches
if color-patches-by = "black"
[
ask patches [ set pcolor black ]
]
if color-patches-by = "profit"
[
ask patches with [profit >= 0]
[
set pcolor scale-color cyan profit 0 max-profit
]
ask patches with [profit < 0 ]
[
set pcolor scale-color pink profit 0 (2 * min-profit)
]
]
if color-patches-by = "risk"
[
ask patches
[
ifelse (risk-min < risk-max) and (risk-min > 0)
[
set pcolor scale-color red (ln annual-risk) (ln risk-max) (ln risk-min)
]
[
set pcolor scale-color red (annual-risk) 1 0
]
]
]
if color-patches-by = "expected utility"
[
ask patches
[
set pcolor scale-color magenta (utility-for nobody) 0 g-max-utility
]
]
end
to color-turtle [max-wealth]
if color-turtles-by = "wealth"
[
set color scale-color green wealth 0 (max-wealth * 1.2)
]
if color-turtles-by = "red"
[
set color red
]
if color-turtles-by = "yellow"
[
set color yellow
]
end
to color-turtles
let max-wealth g-max-wealth
if max-wealth = 0 [ set max-wealth 0.1 ]
ask turtles
[
color-turtle max-wealth
]
end
to output
set g-mean-wealth mean [wealth] of turtles
set g-max-wealth max [wealth] of turtles
set g-max-utility max [utility-for nobody] of patches
set-current-plot "Utility Histogram"
histogram [current-utility] of turtles
let mean-wealth mean [wealth] of turtles
set-current-plot "Mean Wealth (thousands)"
plot mean-wealth / 1000
set-current-plot "Mean Profit"
plot mean [profit] of turtles
set-current-plot "Mean Risk"
plot mean [annual-risk] of turtles
set-current-plot "Std. Dev. of Wealth (thousands)"
plot standard-deviation [wealth] of turtles / 1000
end
to draw-links
ifelse show-links?
[ ask links [ show-link ]]
[ ask links [ hide-link ]]
end]]>mean [wealth] of turtles / 1000standard-deviation [wealth] of turtles / 1000mean [utility-for self] of turtles / 1000business-failures _T_
where _U_ is expected utility for the patch, _W_ is the investor's current value, and _P_ and _F_ are defined above. The term ( _W_ + _T_ _P_) estimates the investment value at the end of the time horizon. The term (1 - _F_) _T_ is the probability of surviving failure over the time horizon; it reduces utility more as failure risk increases. (Economists might expect to use a utility measure such as present value that includes a discount rate to reduce the value of future profit. We ignore discounting to keep this model simple.)
Prediction: The fitness measure includes an explicit forecast of utility over a time horizon that uses the assumption that _P_ and _F_ do not change over time. This assumption is accurate here because the patches' _P_ and _F_ values are static.
Sensing: The investor agents are assumed to know the profit and risk at their own patch and the adjacent neighbor patches, without error.
Interaction: The investors interact with each other only indirectly via competition for patches: an investor cannot reposition itself into a patch that is already occupied by another investor. Investors execute their repositioning action in randomized order, so there is no hierarchy in this competition: investors with higher investment value have no advantage over others in competing for locations.
Stochasticity: The initial state of the model is stochastic: the values of _P_ and _F_ of each patch, and initial investor locations, are set randomly. Stochasticity is thus used to simulate an investment environment where alternatives are highly variable and risk is not correlated with profit. The values of _P_ are drawn from an exponential distribution, which produces many patches with low profits and a few patches with high profits. The random exponential distribution of _P_ makes the results of this model especially variable, even among model runs with the same parameter values.
Whether each investor fails each year is also stochastic, a simple way to represent risk. The investor reposition action uses stochasticity only in the very unlikely event that more than one potential destination patch offers the same highest utility; when there is such a tie the agent randomly chooses one of the tied patches to move to.
Observation: The View shows the location of each agent on the investment landscape. Having the investors put their pen down lets us observe how many patches each has used. Graphs show the mean risk and mean profit of patches occupied by investors, and mean investor wealth over time. The standard deviation in wealth is also graphed as a simple and appropriate measure of how evenly wealth is distributed among investors.
Learning and collectives are not represented.
## 5. Initialization
The value of _P_ for each patch is drawn from a random exponential distribution, which produces more patches with low profits and fewer patches with high profits. The mean of this exponential distribution is 5000. The value of _F_ for each patch is drawn randomly from a uniform real number distribution with minimum of 0.01 and maximum of 0.1.
Twenty five investor agents are initialized and put in random patches, but investors cannot be placed in a patch already occupied by another investor. Their wealth state variable _W_ is initialized to zero.
## 6. Input data
No time-series inputs are used.
## 7. Submodels
**Investor repositioning:** An investor identifies all the businesses that it could invest in: any of the neighboring eight (or fewer if on the edge of the space) patches that are unoccupied, plus its current patch. The investor then determines which of these alternatives provides the highest value of the utility function, and moves (or stays) there.
**Accounting:** This action is fully described above ("Process overview and scheduling").
This file provided as instructor materials for Railsback & Grimm 2018.]]>setup repeat 75 [ go ]setupgomean [wealth] of turtlesstandard-deviation [wealth] of turtlessetupgomean [wealth] of turtlesstandard-deviation [wealth] of turtlesmean [profit] of patches with [any? turtles-here]mean [annual-risk] of patches with [any? turtles-here]setupgomean [wealth] of turtlesstandard-deviation [wealth] of turtlesmean [profit] of patches with [any? turtles-here]mean [annual-risk] of patches with [any? turtles-here]setupgomean [wealth] of turtlesstandard-deviation [wealth] of turtlesmean [profit] of patches with [any? turtles-here]mean [annual-risk] of patches with [any? turtles-here]setupgomean [profit] of turtlesmean [annual-risk] of turtlesmean [wealth] of turtlesstandard-deviation [wealth] of turtles