58
X-Machines for Agent-Based Modeling: FLAME Perspectives
F u n c t i o n 1
F u n c t i o n 2
M e s s a g e o u t t o m e s s a g e b o a r d
M e s s a g e i n f r o m m e s s a g e b o a r d
M e s s a g e s c o p i e d t o
m e s s a g e b o a r d m e m o r y
( A l l a g e n t s h a v e d o n e t h i s )
M e s s a g e b o a r d l o c k e d
( s y n c h r o n i s a t i o n p o i n t )
M e s s a g e b o a r d u n l o c k e d
M e s s a g e s c o p i e d f r o m
m e s s a g e b o a r d t o a g e n t
m e m o r y
T i m e l i n e
FIGURE 3.14: Timeline showing when the synchronization point occurs
when messages interact with functions.
3.5 FLAME’s Missing Functionality
Using the X-machine approach provides agents with much needed complexity to model and simulate complex models. However, there are a number
of advantages other frameworks provide, which FLAME currently does not.
This makes it necessary for modelers to add more complex code, embedding
complex behavior into agents.
Static global conditions. Agents behave in a world with no changing conditions, as there is no global environment agent acting as the world.
None of their decisions have any effect ‘on the world’. It only acts as a
space. This requires a central agent to be programmed into the system
if the model needs a world representation.
No learning or adaptation in agent functions. Agents cannot learn about
their performance and adapt to new conditions. This would require additional programming to add a reward function and complex function
choices to show adaptation.
Assumption of perfect rationality in agents. Agents have access to
complete message boards and perfect knowledge, unless randomness is
added to the message choice.
• Modelers assume agents have perfect knowledge of the past and the
present, including the model they exist in.
X-Machines for Agent-Based Modeling: FLAME Perspectives
F u n c t i o n 1
F u n c t i o n 2
M e s s a g e o u t t o m e s s a g e b o a r d
M e s s a g e i n f r o m m e s s a g e b o a r d
M e s s a g e s c o p i e d t o
m e s s a g e b o a r d m e m o r y
( A l l a g e n t s h a v e d o n e t h i s )
M e s s a g e b o a r d l o c k e d
( s y n c h r o n i s a t i o n p o i n t )
M e s s a g e b o a r d u n l o c k e d
M e s s a g e s c o p i e d f r o m
m e s s a g e b o a r d t o a g e n t
m e m o r y
T i m e l i n e
FIGURE 3.14: Timeline showing when the synchronization point occurs
when messages interact with functions.
3.5 FLAME’s Missing Functionality
Using the X-machine approach provides agents with much needed complexity to model and simulate complex models. However, there are a number
of advantages other frameworks provide, which FLAME currently does not.
This makes it necessary for modelers to add more complex code, embedding
complex behavior into agents.
Static global conditions. Agents behave in a world with no changing conditions, as there is no global environment agent acting as the world.
None of their decisions have any effect ‘on the world’. It only acts as a
space. This requires a central agent to be programmed into the system
if the model needs a world representation.
No learning or adaptation in agent functions. Agents cannot learn about
their performance and adapt to new conditions. This would require additional programming to add a reward function and complex function
choices to show adaptation.
Assumption of perfect rationality in agents. Agents have access to
complete message boards and perfect knowledge, unless randomness is
added to the message choice.
• Modelers assume agents have perfect knowledge of the past and the
present, including the model they exist in.