A study on swarm intelligence in group decision making of farmers’ self help groups in Uttarakhand
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Date
2022-03
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
The concept and utility of SHGs is a proven fact and it has contributed significantly in group-led extension activities on a large scale. However, these SHGs play instrumental role in empowering its members and in many core aspects of farming, but these groups also face various challenges among which ineffective group decision making is prominent. Therefore, it is crucial and critical to strengthen the self help groups through resolving the issues of stability for strengthening the extension system in light of agriculture and rural development at national level. In this context, it is important to critically analyze the natural mapping of group decision making which is observed in various forms in the nature, especially in the swarms with distinction of swarm-intelligence. Looking into the parallel world of honey-bees, swarm-intelligence is a phenomenon through which the swarm of bees perform various group tasks and take crucial group decisions effectively and efficiently without any central control of the leader utilizing their collective intelligence. Therefore, the study was intended to map the group decision processes among honey bee swarms and its inbuilt influences to relate it with the decision making processes among SHGs to study its impact so that the speed and accuracy of group decision making in SHGs could be enhanced and the conflicts in decision making could be constructively utilized. The study was conducted on swarm intelligence in group decision making of farmers’ self-help groups of Nainital and Udham Singh Nagar districts of Uttarakhand. Total 12 SHGs and 120 respondents (10 from each SHG) were selected. Frequency, percentage, arithmetic mean, standard deviation, co-efficient of correlation, significance of correlation coefficient, multiple linear regression analysis and Analysis of variance (ANOVA) were used to analyze the data for meaningful interpretation. Profile characteristics of the members of selected SHGs revealed that majority of the members were middle aged, married females, marginal farmers, educated up to intermediate level and were engaged in agriculture with other subsidiary occupation. Maximum of the respondents belonged to medium category in terms of annual income, achievement motivation, ease of use of android phones, duration (between 3 to 5years) of membership in SHGs, task function and maintenance function. They had favourable attitude towards collectivisation and group decision making. Regarding results obtained through group decision making index, it was inferred that majority of the members of SHGs’ scored at medium level on group decision making index (GDMI). With respect to the ten indicators of group decision making it was observed that the respondents scored at medium level for each indicator. It was also reported that there was significant variation in group decision making among the selected SHGs. It was inferred from the correlation analysis, that all the ten indicators of Group Decision Making were significantly and positively related to Group Decision Making Index. It was also concluded that 90.60 per cent of variation in the dependent variable i.e., Group-decision making was explained by the ten indicators of group decision making index. Thus, it was inferred that the ten indicators play a significant role in shaping Group decision making. In relation to the effectiveness of Swarm A.I. based group decision making, it was inferred that seven key factors i.e., Decision impulse, Conviction of the decision, Engagement in Decision, Decision duration, Decision Alignment, Extent of Participation and Real-time physical negotiation contributed to the effectiveness of Swarm A.I. software. Swarm AI was found aligned to the swarm intelligence mechanism in honey bee swarms wherein honey bees hold an open and free competition of ideas to reach to a particular decision. Optimal decision-making framework developed in the present study highlighted that groups must reach a decision not based on individual responses of the members, but based on the changing dynamics of the entire system to reach upto the most agreeable option. The framework was formulated by integrating the seven factors of Swarm A.I. based decision making with the ten indicators of Group decision making index. The study concluded that the elements and components of swarm-intelligence could be effectively mapped to generate an effective system or software to ignite the group decision making capability of SHGs and to extend it across the SHGs throughout country. It was also inferred that through the mapping the conflicts in decision making process could be effectively translated into universally accepted and effective group decisions to lead into sustained and impactful structures of SHGs.