Abstract
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context‐dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation‐free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta‐analyses.
Highlights
Research synthesis is an essential scientific endeavor that integrates and assesses disparate data, concepts, and/or theories to yield novel insights or explanations[1] by consolidating collected evidence.[2,3] It is expected to contribute to fostering evidence-based research, policy, and practice.[4,5]Hypotheses and ideas that express a simple, yet general, statement are attractive in scientific disciplines studying highly context-dependent systems
Several synthesis studies suggested that the validity of this hypothesis is dependent on the context, determined by species' identity, ecosystem type, ecological level, and test method.[8,14,15,16,17]
We show that machine learning is a promising tool in research synthesis for integrating collected evidence for discovering novel patterns and for finding the source of controversy in a general hypothesis
Summary
In these disciplines, such general hypotheses must be tested repeatedly from various perspectives, under different contexts, and using different methods. An example is the enemy release hypothesis, one of the most prominent hypotheses to explain biological invasions.[6,7,8,9,10,11,12] The hypothesis posits that the absence of enemies in the exotic range of non-native species determines their invasion success.[13,14] Several synthesis studies suggested that the validity of this hypothesis is dependent on the context, determined by species' identity, ecosystem type, ecological level, and test method.[8,14,15,16,17]
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