Are species intelligent?

Schull, Jonathan

doi:10.1017/s0140525x00077542

Cited by 75 publications

(19 citation statements)

References 95 publications

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“…If lineages now present are a biased set of the lineages there have been, and lineages with this property are over-represented in the survivors, then the case for lineage adaptation is strong. Schull (1990) asked whether species were intelligent. I used to think he gave the wrong answer; I now think he asked the wrong question.…”

Section: Complex Dependence and Clade Selectionmentioning

confidence: 99%

Explanatory pluralism in evolutionary biology

Sterelny

1996

Biol Philos

118

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The ontological dependence of one domain on another is compatible with the explanatory autonomy of the less basic domain. That autonomy results from the fact that the relationship between two domains can be very complex. In this paper I distinguish two different types of complexity, two ways the relationship between domains can fail to be transparent, both of which are relevant to evolutionary biology. Sometimes high level explanations preserve a certain type of causal or counterfactual information which would be lost at the lower level; I argue that this is central to the proper understanding of the adaptationist program. Sometimes high level kinds are multiply realised by lower level kinds: I argue that this is central to the understanding of macroevolution.

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Section: Complex Dependence and Clade Selectionmentioning

confidence: 99%

Explanatory pluralism in evolutionary biology

Sterelny

1996

Biol Philos

118

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“…This as pointed out by Vertosick (2002) is simple brain chauvinism, whilst the psychologist Schull (1990) asserts this view to be both anthropocentric and asserting nerve cells as possessing some sort of vitalist quality. Warwick (2001), an artificial intelligence expert also considers this to be "subjective intelligence."…”

Section: Signal Transduction In Plant Cellsmentioning

confidence: 98%

“…Species intelligence (Schull 1990). "Plant and animal species are information processing entities of such complexity and adaptive competence that it may be fruitful to consider them intelligent."…”

Section: Signal Transduction In Plant Cellsmentioning

confidence: 99%

Plant intelligence

Trewavas

2005

Naturwissenschaften

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Intelligent behavior is a complex adaptive phenomenon that has evolved to enable organisms to deal with variable environmental circumstances. Maximizing fitness requires skill in foraging for necessary resources (food) in competitive circumstances and is probably the activity in which intelligent behavior is most easily seen. Biologists suggest that intelligence encompasses the characteristics of detailed sensory perception, information processing, learning, memory, choice, optimisation of resource sequestration with minimal outlay, self-recognition, and foresight by predictive modeling. All these properties are concerned with a capacity for problem solving in recurrent and novel situations. Here I review the evidence that individual plant species exhibit all of these intelligent behavioral capabilities but do so through phenotypic plasticity, not movement. Furthermore it is in the competitive foraging for resources that most of these intelligent attributes have been detected. Plants should therefore be regarded as prototypical intelligent organisms, a concept that has considerable consequences for investigations of whole plant communication, computation and signal transduction.

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“…However it has long beenappreciated that there are many ways in which viewing biological systems as involving searches over such functions can lead to deeper understanding of them 238,290]. Conversely, some have argued that the mechanism underlying biological systems can beused to help design search algorithms 124].…”

Section: Biologically Inspired Systemsmentioning

confidence: 99%

Theory of Collective Intelligence

Wolpert

2004

Collectives and the Design of Complex Systems

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This paper surveys the emerging science of how to design a \COllective INtelligence" (COIN). A COIN is a large multi-agent system where: i) There is little to no centralized, personalized communication and/or control. ii) There is a provided world utility function that rates the possible histories of the full system.In particular, we a r e i n terested in COINs in which e a c h a g e n t runs a reinforcement learning (RL) algorithm. The conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather, that approach begins with explicit modeling of the dynamics of the overall system, followed by detailed hand-tuning of the interactions between the components to ensure that they \cooperate" as far as the world utility i s concerned. This approach is labor-intensive, often results in highly nonrobust systems, and usually results in design techniques that have limited applicability. In contrast, we w i s h t o s o l v e the COIN design problem implicitly, via the \adaptive" character of the RL algorithms of each of the agents. This approach i n troduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we d o n o t have phenomena like t h e tragedy of the commons, Braess's paradox, or the liquidity trap? Although still very young, research speci cally concentrating on the COIN design problem has already resulted in successes in arti cial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's El Farol bar problem. It is expected that as it matures and draws upon other disciplines related to COINs, this research will greatly expand the range of tasks addressable by human engineers. Moreover, in addition to drawing on them, such a fully developed science of COIN design may provide much insight into other already established scienti c elds, such as economics, game theory, and population biology.

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Are species intelligent?

Cited by 75 publications

References 95 publications

Explanatory pluralism in evolutionary biology

Explanatory pluralism in evolutionary biology

Plant intelligence

Theory of Collective Intelligence

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