The Effects of Affective Social Bonds on the Interactions and Survival of Simulated Agents

Khan, Imran; Lewis, Matthew; Cañamero, Lola

doi:10.1109/aciiw.2019.8925031

Cited by 2 publications

(8 citation statements)

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“…In all of these works, however, the effects and analysis of these social bonds have been at a dyadic level (a human-agent pair bond), as opposed to the group or social level. Attempting to address those limitations, our previous work has investigated some of the adaptive effects of affective social bonds in a society of artificial (virtual) agents (Khan et al, 2020(Khan et al, , 2019Khan and Cañamero, 2021).…”

Section: Related Workmentioning

confidence: 99%

“…The simulation environment was developed using the NetLogo platform, version 5.3.1 (Wilensky, 1999), modelled as a simple, enclosed, two-dimensional world (of size 99 × 99 units). Mirroring our previous environments (Khan et al, 2020(Khan et al, , 2019, the environment consisted of two types of objects (resources): autonomous Agents, and Food. A screenshot of the complete environment can be seen in Figure 2.…”

Section: Simulation Environmentmentioning

confidence: 99%

“…Experiments were conducted in the simulation environment described in Section 2. Following the approach in our previous work (Khan et al, 2020(Khan et al, , 2019Khan and Cañamero, 2021), we used a society of six artificial agents, where three agents share affective social bonds and the remaining three are unbonded. We have found that this group size (six agents) to be a suitable number of agents that 1) allows the society to be split into multiple (non-dyad) groups, 2) permits for the appropriate level of complexity for analysis, and 3) allows for analysis at the levels of the overall society, the sub-groups, and the individual agents (which we call the macro, meso, and micro-levels, respectively).…”

Section: Experimental Conditionsmentioning

confidence: 99%

“…Building on our own previous work ( Khan et al, 2019 , 2020 ; Khan and Cañamero, 2021 ), and also following the approach that has been used extensively in related approaches ( Lewis and Cañamero, 2019 ; Lones et al, 2017 ; Cañamero and Lewis, 2016 ; Montebelli et al, 2008 ; Iizuka and Di Paolo, 2008 ), our agent model accounts for several biologically-inspired abstractions of hormonal mechanisms that act as (hormonal) modulators on an agent’s physiology and behaviours. These hormonal mechanisms are inspired by two biological hormones: cortisol (CT) and oxytocin (OT).…”

Section: Agent Simulationmentioning

confidence: 99%

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The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

Khan

Cañamero

2022

Front. Robot. AI

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In dynamic (social) environments, an affective state of “stress” can be adaptive and promote agent wellbeing, but maladaptive if not appropriately regulated. The presence of (and interactions with) affect-based social support has been hypothesised to provide mechanisms to regulate stress (the “social buffering” hypothesis), though the precise, underlying mechanisms are still unclear. However, the hormone oxytocin has been implicated in mediating these effects in at least two ways: by improving social appraisals and reducing the short-term release of stress hormones (i.e., cortisol), and adapting an agent’s long-term stress tolerance. These effects likely facilitate an agent’s long-term adaptive ability by grounding their physiological and behavioural adaptation in the (affective) social environment, though these effects also appear to be context-dependent. In this paper, we investigate whether two of the hypothesised hormonal mechanisms that underpin the “social buffering” phenomenon affect the long-term wellbeing of (artificial) social agents who share affective social bonds, across numerous social and physical environmental contexts. Building on previous findings, we hypothesise that “social buffering” effects can improve the long-term wellbeing of agents who share affective social bonds in dynamic environments, through regular prosocial interactions with social bond partners. We model some of the effects associated with oxytocin and cortisol that underpin these hypothesised mechanisms in our biologically-inspired, socially-adaptive agent model, and conduct our investigation in a small society of artificial agents whose goal is to survive in challenging environments. Our results find that, while stress can be adaptive and regulated through affective social support, long-term behavioural and physiological adaptation is determined by the contextual perception of affective social bonds, which is influenced by early-stage interactions between affective social bond partners as well as the degree of the physical and social challenges. We also show how these low-level effects associated with oxytocin and cortisol can be used as “biomarkers” of social support and environmental stress. For socially-situated artificial agents, we suggest that these “social buffering” mechanisms can adapt the (adaptive) stress mechanisms, but that the long-term efficacy of this adaptation is related to the temporal dynamics of social interactions and the contextual perception of the affective social and physical environments.

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Section: Related Workmentioning

confidence: 99%

Section: Simulation Environmentmentioning

confidence: 99%

Section: Experimental Conditionsmentioning

confidence: 99%

Section: Agent Simulationmentioning

confidence: 99%

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The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

Khan

Cañamero

2022

Front. Robot. AI

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“…Using the long-standing approach from our research group (Cañamero, 1997;Lones et al, 2017;Lewis and Cañamero, 2019;Khan et al, 2019), the behaviours of our artificial agents are grounded in the regulation of a homeostatically-controlled internal "physiology" through the Action-Selection Architecture (ASA). The ASA selects actions to satisfy one of two physiological needs: Energy (a physical, survival-critical need) and SocialNeed (a noncritical need for social contact) (Table 1).…”

Section: Action-selection Architecturementioning

confidence: 99%

Adaptation-By-Proxy: Contagion Effect of Social Buffering in an Artificial Society

Khan¹,

Cañamero²

2021

The 2021 Conference on Artificial Life

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The "social buffering" phenomenon proposes that social support facilitates wellbeing by reducing stress in a number of different ways. While this phenomenon may benefit agents with social support from others, its potential effects on the wider social group are less clear. Using a biologicallyinspired artificial life model, we have investigated how some of the hypothesised hormonal mechanisms that underpin the "social buffering" phenomenon affect the wellbeing and interactions of agents without social support across numerous social and physical contexts. We tested these effects in a small, rank-based society, with half of the agents endowed with numerous hormonal mechanisms associated with "social buffering", and half without. Surprisingly, our results found that these "social buffering" mechanisms provided survivalrelated advantages to agents without social support across numerous conditions. We found that agents with sociallyadaptive mechanisms themselves become a proxy for adaptation, and suggest that, in some (artificial) societies, "social buffering" may be a contagious phenomenon.

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The Effects of Affective Social Bonds on the Interactions and Survival of Simulated Agents

Cited by 2 publications

References 34 publications

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

Adaptation-By-Proxy: Contagion Effect of Social Buffering in an Artificial Society

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