3 Optimization Principles
Optimization is an umbrella term comprising all processes, alterations in technology
or methods that make things better—more functional, more effective, more streamlined, more efficient, and so on. It includes anthropocentric values such as beauty
and pleasure, as well as more objective values such as speed or longevity. The term
is used in different sciences, e.g., in economics, politics, technology, computer
science, mathematics and ecology (Goh and Tan 2009; Battiti et al. 2008; Meszena
et al. 2001; Dixit 1990). The meaning, relationships and differences between the
terms adaptation, trait, learning and optimization are reviewed in Sayed (2014) and
Baluska et al. (2018). Optimization in many instances is defined broadly and
comprises processes such as adaptation and habituation (Gagliano et al. 2014).
In nature we can find optimization as a trend of many processes working at
different levels of organization, during ecological and evolutionary timescales. The
goal of optimization is optimality, i.e. reaching a minimum, maximum, or the best fit
or functionality (e.g., the top of a bell-curve). Optimality would mean that all parts in
a system are as perfect as they can be, which would imply no necessity for change.
Whenever we observe change, this fact alone indicates that not everything was
perfect. Optimality is a relative term. If something is optimal it is only optimal in
relation to certain conditions. If the conditions change the processes have to be
adjusted to reach optimality again. Thus, optimality normally is a rather ephemeral
event of any given system that is subject to change (cf. Richardson 1994; Cody
1974).
Optimization of processes implies motivation (caused by convictions or emotions, e.g., avoidance of thirst or pain), competition (e.g. plants and animals—for
resources including space) or cooperation (e.g. within human or vertebrate work
groups and plant communities as well; e.g. Wheeler et al. 2015, Heard and Remer
2008, Trepl 2005, Thienemann 1939, 1956).
For example, at the heart of consumer theory is the assumption that consumers are
individuals that try to enhance their utility, freedom, fun or whatever they pursue. In
a model with several goods in which a consumer has a budget of a certain amount, it
is assumed that he will choose the combination of goods with the highest effect in
relation to his own goals. However, optimizing several subsystems independently
will not in general lead to optimality of the whole system (Dixit 1990).
Optimization theory in evolutionary ecology dates back to Darwin’s idea of
favourable conditions of life, ecological variability, natural selection and survival
of the fittest which he expressed in his report and subsequent biogeographical
analyses from the voyage of the Beagle (Darwin 1839). Since then different optimization theories in biology and macroecology have emerged. Natural selection,
survival of the fittest, genetic variability, optimal foraging, canopy photosynthesis,
energy efficiency and maximum entropy production are keywords that exemplify
optimization in its ecological context (Dewar 2010; Braakhekke and Hooftman
1999; Richardson 1994; Reiss 1987; Darwin 1859).
Resources for Humans, Plants and Animals: Who Is the Ruler of the Driver? And:. . .
85
Optimization is an umbrella term comprising all processes, alterations in technology
or methods that make things better—more functional, more effective, more streamlined, more efficient, and so on. It includes anthropocentric values such as beauty
and pleasure, as well as more objective values such as speed or longevity. The term
is used in different sciences, e.g., in economics, politics, technology, computer
science, mathematics and ecology (Goh and Tan 2009; Battiti et al. 2008; Meszena
et al. 2001; Dixit 1990). The meaning, relationships and differences between the
terms adaptation, trait, learning and optimization are reviewed in Sayed (2014) and
Baluska et al. (2018). Optimization in many instances is defined broadly and
comprises processes such as adaptation and habituation (Gagliano et al. 2014).
In nature we can find optimization as a trend of many processes working at
different levels of organization, during ecological and evolutionary timescales. The
goal of optimization is optimality, i.e. reaching a minimum, maximum, or the best fit
or functionality (e.g., the top of a bell-curve). Optimality would mean that all parts in
a system are as perfect as they can be, which would imply no necessity for change.
Whenever we observe change, this fact alone indicates that not everything was
perfect. Optimality is a relative term. If something is optimal it is only optimal in
relation to certain conditions. If the conditions change the processes have to be
adjusted to reach optimality again. Thus, optimality normally is a rather ephemeral
event of any given system that is subject to change (cf. Richardson 1994; Cody
1974).
Optimization of processes implies motivation (caused by convictions or emotions, e.g., avoidance of thirst or pain), competition (e.g. plants and animals—for
resources including space) or cooperation (e.g. within human or vertebrate work
groups and plant communities as well; e.g. Wheeler et al. 2015, Heard and Remer
2008, Trepl 2005, Thienemann 1939, 1956).
For example, at the heart of consumer theory is the assumption that consumers are
individuals that try to enhance their utility, freedom, fun or whatever they pursue. In
a model with several goods in which a consumer has a budget of a certain amount, it
is assumed that he will choose the combination of goods with the highest effect in
relation to his own goals. However, optimizing several subsystems independently
will not in general lead to optimality of the whole system (Dixit 1990).
Optimization theory in evolutionary ecology dates back to Darwin’s idea of
favourable conditions of life, ecological variability, natural selection and survival
of the fittest which he expressed in his report and subsequent biogeographical
analyses from the voyage of the Beagle (Darwin 1839). Since then different optimization theories in biology and macroecology have emerged. Natural selection,
survival of the fittest, genetic variability, optimal foraging, canopy photosynthesis,
energy efficiency and maximum entropy production are keywords that exemplify
optimization in its ecological context (Dewar 2010; Braakhekke and Hooftman
1999; Richardson 1994; Reiss 1987; Darwin 1859).
Resources for Humans, Plants and Animals: Who Is the Ruler of the Driver? And:. . .
85