Behavioural modeling

There you are, looking for a job, and one of the requirements is "experience in behavioural modeling".  What does that actually mean? 

The first thing to determine is what are behaviours.  Behaviours, or behaviors for the Americans among us, are easily understood but harder to define. Purely based on the linguistic definition, Oxford Languages says they are "the way in which one acts or conducts oneself, especially towards others" but also "the way in which an animal or person behaves in response to a particular situation or stimulus." Lee Alan Dugatkin agrees with the latter, formulating that "behavior is the coordinated responses of whole living organisms to internal and/or external stimuli." A behavior happens where there is a need "If an animal has a need, its motivational state is affected so that behavioral and physiological responses that should result in remedying that need can be made." (D. M. Broom).

It seems to me that behaviors are coordinated movements and acts, directed to addressing a need. Some behaviors are learned (styles of playing, replies to human commands), and some are inherited (searching for a teat after birth). 

Now that we know about behaviors, let's see what are models? Modeling means to build models, abstract representations of complex truths. A model can be a simplification, and can fit to all instances of a repeating concept. A model can be used to compare representations across species or in time.  Schank, Joshi and May write that "Models can be physical, symbolic, mathematical, or computational, but they are always simpler than the animal systems they represent." Clearly then there are several methods of modelling, and the best fit can be chosen based on the goal of the research. If we intend to use behavioral data in a computer system, a mathematical or computational description would seem the most suitable. If we intend to demonstrate the behavior to others or even mimic it, physical modelling is the way to go.  

Schank et. al. also give models nine dimensions:

1. Realism
2. Detail
3. Generality
4. Match
5. Precision: how quantitatively precise a model is in it's predictions  
6. Tractability: how analyzable or manipulable a model is
7. Integration: Can it be used together with other models?
8. Level: Cellular, population, strain...
9. Medium

Let's take a look at some models to better understand how they really could look like.  The first example is a hidden Markov model (HMM) from the publication of Leos-Barajas, Gangloff, Adam et. al (2017).  This is a mathematical model. It looks complicated, and will require understanding about the mathematical notation and statistics to understand and to apply. 

Another beautiful example is from Ellen Evers from the University of Utrecht. In her presentation she explains the differences of Agent-based models (ABM) and Ordinary differential equations (ODE). 

An ABM looks at individuals: how each individual moves and acts during the behavior. It is less effective in modeling the group. An ODE looks at a group and determines the behavior of the group as a whole, losing sight of each individual. Her example uses the balance of wolves and sheep. Sheep are increased by births, and decreased by predation by wolves. Wolves are increased by predation, and decreased by natural death. 

In ODE terms the  models are

Sheep = + birth*Sheep – pred.*Sheep*Wolves  

Wolves = + pred.*Sheep*Wolves – death*Wolves

But for ABM the rules are for each individual:

SHEEP: If I meet wolve: die!

With chance = birthrate: Reproduce!

WOLVES: If I meet sheep: energy +1

If energy (from sheep) = 0: die!

With chance = birthrate: Reproduce

The big difference is that ODE model is deterministic. It can be used to predict situations when some variables are known. It could change what happens when there are 100 wolves and 50 sheep in comparison to a situation with 100 sheep and 50 wolves (in both cases, it's not looking good for the sheep.) ODE is not spatial (measuring things in terms of movements in space) and requires homogenity. ABM, on the other hand, just models the situation. It is applicable to heterogenous populations. 

I hope that this short introduction has given you some insight into modeling animal behavior. It is a complex field, and the lesson from Ellen Evers is heartily recommended as a good starting point for understand the differences of ABM and ODE.

More information

Lee Alan Dugatkin: What is "behavior", anyway?

D. M. Broom Animal welfare: concepts and measurement

Schank, Joshi, May et. al. Multi-modeling approach

Leos-Barajas, Gangloff, Adam et. al: Multi-scale modeling of animal movement and general behavior data using Hidden Markov models with hierarchical structures

Ellen Evers: ABM and ODE