This is part of a contribution to Section 63 of the ECPR 2020 conference. In terms of the COVID-19 pandemic, the conference (planned in Innsbruck) has become a “web thing”. Section 63 addresses the dynamics of political movements and thus also the research methods that go with them. Our assumption is that operational state structures (sovereign jurisdictions) are grouped political movements (institutions) that connect each other as well as individuals in networks. These networks allow top-down (state → institution → individual), bottom-up (individual → institution → state) and horizontal (state → state, institution → institution and individual → individual) communications and transactions. Agent-based simulation (ABMS) is the obvious choice for studying them [De Marchi (2005)].
A further assumption is that at least half of the knowledge required for informed political decision-making can be collected from academic sources, while the other half must be acquired ad hoc because political situations require not only general but also local knowledge. For citizens entitled to vote, legislators and enforcers, this raises a serious question in the direction of science:
Are there cost-effective quality methods and tools to combine various research results to quickly estimate the impact of an intended political decision on society?
Over the past decade, we have been thinking about this question when approaching our various research projects, for example: parole in the Netherlands [Schmidt (1987)], file-sharing and copyright in the West [Schmidt et. al. (2007)], EU privacy in the PRC [Zhang (2014)], failing ICT in the Dutch judiciary [Schmidt and Zhang 2019] and weaknesses in ICT for cross-border communication between legal professionals in Europe [Schmidt (2019)] . We came to the understanding that jurisdictions are always complex adaptive networked systems – and thus suitable for the ABMS approach. But with that we immediately have an answer to the question:
no, there are no such cost-effective quality methods and instruments in 2019
because ABMS methods and techniques are now (we all still know the quote from 1651) in a state comparable to what Hobbes ascribes to man when he is the plaything of unregulated human nature:
In such condition, there is no place for industry; because the fruit thereof is uncertain: and consequently no culture of the earth; no navigation, nor use of the commodities that may be imported by sea; no commodious building; no instruments of moving, and removing, such things as require much force; no knowledge of the face of the earth; no account of time; no arts; no letters; no society; and which is worst of all, continual fear, and danger of violent death; and the life of man, solitary, poor, nasty, brutish, and short.
In short, if we paraphrase Hobbes in this way, there is no definite place for ABMS as a research method when it must come to fruition in ‘such a condition.‘ Hobbes refers to the state of chaos, of wars of everyone against everyone, a state that in his opinion always follows when societies do not have a public sector. For ABMS, that would mean chaos, a general state of free struggle between the other research methods dealing with political issues without a referee mechanism. And it is true. If one thing becomes clear during the coronavirus pandemic, it is that the PRC-US relations are in such a state of free wrestling – even that this situation also arises in the regulated public relations between member states and the community (EU) and states and federation (USA).
Based on our experiences, we sought for and designed a framework for situated political research through agent-based models that combine rules with stochastic distribution-forming mechanisms. We call it Epiframer. We (want to) illustrate it and its pros and cons using the timeline (April 2017 to August 2020) of trade war dynamics in the free struggle between the US and the PRC. We are aware that what we want cannot be completely accomplished. However, we do expect that we can come a little closer to a positive answer to the question formulated above.