Since 1990 we have been involved in modeling decision-making, particularly in fish and plankton. Much of this work is based on a theoretically derived model for visual range of aquatic organisms (Aksnes & Giske 1993, Aksnes & Utne 1997), which again has allowed calculations of feeding rates and predation risks (Giske & al. 1994, Fiksen & al. 2002).
We have been using Life History Theory (Aksnes & Giske 1990, Giske & Aksnes 1992, Salvanes & al. 1994, Giske & Salvanes 1995, Eiane & al. 1998), Game Theory (Giske & al. 1997) and State-Dependent Optimization (Giske & al. 1992, Rosland & Giske 1994, 1997, Fiksen & Giske 1995, Fiksen 1997, Rosland 1997, Fiksen & Carlotti 1998, Kirby & al. 2000) to model both short-term and life-history decisions. More recently, we have been using Genetic Algorithms to evolve adaptive behaviors in Individual-Based Models, either directly as life-history decision genes, neural networks of brains, or decisions coming out from the proximate architecture for behavioural control of the individuals. We have also performed field and lab studies to test assumptions in the models.
In a series of papers (Eliassen & al. 2006, 2007, 2009) we have studied what conditions favour evolution of learning, and under which circumstances the learning strategy is better or worse than the non-learning alternative. Our key assumption is that individual learning through exploration incurs a time-cost relative to the innate or genetically fixed strategy. Some situations allow coexistence of learners and non-learners in the same population, while life expectancy may be an important determinant for the adaSptive value of learning.
The Genetic Algorithm has been a much used research tool in the group for more than a decade. While several papers have used the GA to evolve adaptive values of neural networks or animal decison architectures, the GA can also be used directly to model the major life history decisions in an organism, as done by Fiksen (2000) for the copepod Calanus finmarchicus. This method was also used by Strand & al. (2002) for the mesopelagic fish Maurolicus muelleri. Currently, PhD student Selina Våge is using the GA to evolve life styles in microbial ecosystems.
The background for wanting to develop this tool was the complexity of decisions often faced by organisms. While Life History Theory is a good tool for studies of long-term strategic decisions (and also has been used for the short-term by implicitly assuming constant motivations), Game Theory is good for studies of conflict and cooperation between organisms, and State-Dependent Optimization is good for modelling short-term fluctuations in motivation driven by changes in the (physiological) state of the organism. Through this new method we wanted an agent to be able to consider all these aspects simultaneously (Giske & al. 1998, Huse & Giske 1998).
ING consists of an Individual-Based Model where the decisions in each individual is controlled by its Artificial Neural Network which again is inherited from the parents and evolved by a Genetic Algorithm. Its ability to find the optimal solution has been studied by comparing with dynamic programming (Huse & al. 1999). The tool has been used to model capelin distribution in the Barents Sea (Huse & Giske 1998) and vertical migration in mesopelagic fish (Strand & al. 2002).
The model of the Proximate Architecture for Living in Giske & al (2013)
While ING is an excellent tool for modelling complex individual decisions and population consequences, it it not equally suited as a tool for understanding WHY the agents decide as they do. We have therefore tried to develop tools that mimic the proximate architecture for living of a fish and thereby to understand what decisions it makes. A first attempt at this was Giske & al. (2003), but Giske & al. (2013, see figure) gives a more formal yet strongly simplified model of behaviour architecture and their ecological effects. Giske & al. (2014) shows that this architecture also impacts the genetic diversity of populations, and hence their evolvability. More work is ongoing.
These theoretical models have also been used in applied models (Fiksen & MacKenzie 2002, Fiksen & al. 2007, Kristiansen & al. 2007), and applied models have been used to compare and test goodness of fit of different behavioural models in applied ecological situations (Vikebø & al. 2007, Kristiansen & al. 2009). This is a step from individual behaviour to population dynamics, and maybe a step towards ecosystem og fsheries management tools.
In connection with the development of the modelling tools, TEC has utilised field and lab studies either to test model assumptions or to study behaviour that models should be able to capture. Our most common field studies includes ocean optics, vertical behaviour and life cycles of mesopelagic fishes (Kaartvedt & al. 1988, 2005, Giske & al. 1990, Giske & Aksnes 1992, Balino & Aksnes 1993, Rasmussen & Giske 1994, Goodson & al. 1995, Aksnes & al. 2009, Staby & Aksnes 2011) and jellies (Eiane & al. 1999) but also laboratory investigations of vision in fish (Utne & al. 1993, Utne & Aksnes 1994).