A simple interpretation of undirected edges in essential graphs is wrong

Artificial intelligence for causal discovery frequently uses Markov equivalence classes of directed acyclic graphs, graphically represented as essential graphs, as a way of representing uncertainty in causal directionality. There has been confusion regarding how to interpret undirected edges in essential graphs, however. In particular, experts and non-experts both have difficulty quantifying the likelihood of uncertain causal arrows being pointed in one direction or another. A simple interpretation of undirected edges treats them as having equal odds of being oriented in either direction, but I show in this paper that any agent interpreting undirected edges in this simple way can be Dutch booked. In other words, I can construct a set of bets that appears rational for the users of the simple interpretation to accept, but for which in all possible outcomes they lose money. I put forward another interpretation, prove this interpretation leads to a bet-taking strategy that is sufficient to avoid all Dutch books of this kind, and conjecture that this strategy is also necessary for avoiding such Dutch books. Finally, I demonstrate that undirected edges that are more likely to be oriented in one direction than the other are common in graphs with 4 nodes and 3 edges.

Introduction
Directed acyclic graphs (DAGs) are becoming increasingly important in fields such as epidemiology, biostatistics, operations research, and others as a tool for causal modeling and causal inference [1][2][3][4][5][6][7]. Often such graphs are drawn from background knowledge or expert opinion, but there is a substantial literature on statistical artificial intelligence methods that can identify one or more plausible causal models [8][9][10][11][12]. There is growing interest in using methods from this field, termed "causal discovery", but this is hindered by confusion regarding how to interpret their output.
In this paper I shed some light on the interpretation of one of the most common types of output produced by causal discovery algorithms, the essential graph. Specifically, I provide a Dutch book argument that a simple interpretation of undirected edges in essential graphs is not rational. I also provide another theorem proving that a different interpretation can be used as the basis for a betting strategy that is not vulnerable to the same kind of Dutch book. I conjecture that the same betting strategy is also necessary to avoid being Dutch booked, but do not provide a proof for the conjecture. Background Causal graphical modeling stems largely from [1,8]. All of the important concepts used in this paper are covered in, or were introduced in, those publications. Those works make heavy use of statistical notions such as conditional independence, however such notions are not required for the theorems or proofs in this paper. In fact, the reader is required only to understand some limited terminology pertaining to directed graphical models, as well as rudimentary probability theory. The necessary background in graphical models is covered quickly in the Definitions section, while the reader's knowledge of rudimentary probability theory is assumed.

Importance
Causal discovery algorithms are increasingly being used on real world data sets to solve real world problems, e.g. [13][14][15][16][17][18][19][20]. It is rare for the result of such analyses to be a single DAG, so these papers commonly report graphs containing one or more undirected edges. These graphs are thus not DAGs, but more complex graph types such as essential graphs or mixed ancestral graphs [8]. A simple understanding of undirected edges in essential graphs is widely held by those using these methods, namely that "there is an arrow from one of these variables to the other, but it could point in either direction". The more precise implications, however, are often not understood. Misunderstandings about such details are not a significant problem in and of themselves, however they can result in more serious errors, as this paper will show. When agents are trying to estimate precise quantities, such as the probability that an undirected edge in an essential graph has a particular directionality in the underlying DAG, these details become important.
Such estimates are becoming more common as researchers attempt to identify the best practices for using causal discovery algorithms. Methods such as resampling have begun to be evaluated as, and used as, tools for estimating confidence in discovered graphs, e.g. [15,19,21]. Specifically, these methods have been used to estimate the probability that the causal discovery algorithm has correctly identified particular edges. The presence of undirected edges in the discovered graphs produces a dilemma for users of this procedure, however. For example, let X and Y be a pair of variables in an analyzed data set, such that in 60% of the resampled data sets there is an undirected edge X-Y and in 40% there is the edge X ! Y. What type of edge between X and Y should the user have the most confidence in? If we simply take the edge orientation that is most common, then it appears that the undirected edge is most plausible. If, however, we consider the undirected edges to be half directed in either way, then we infer that X ! Y has 70% probability from the resampling analysis, and X ! Y is most plausible. Undirected edges in essential graphs are not defined to have equal probability of being oriented in either direction, however such an interpretation may be tempting due to its simplicity or computational convenience. I argue here that rational agents should avoid this interpretation when possible, and offer another solution to this problem.

Outline
In the Definitions section I define the terms to be used throughout the rest of the paper, and in the section following that I present the Dutch book argument against the equal orientation probabilities interpretation. In the section "Avoiding Dutch books" I prove that a different betting strategy is immune to such Dutch book arguments. In the Conjecture section I conjecture that the proposed betting strategy is also necessary for avoiding such Dutch books. In the section "Prevalence of imbalanced undirected edges" I analyze the specific case of graphs with 4 nodes and 3 edges, and evaluate the prevalence of undirected edges that are more likely to be oriented in one direction than the other. In the Discussion section I discuss the implications of these arguments. Finally, I summarize and conclude the paper in the Conclusion section.

Definitions
This section is provided both as a very brief, rapid, and formal introduction to the topic and as a reference for the later parts of the paper. Many of these concepts have been defined in multiple different ways elsewhere, but the definitions provided here are largely equivalent, and are more suited to the purposes of this paper. Aside from the definition of Dutch book provided at the end, readers who are already familiar with the concepts and statistical properties of directed acyclic graphs, in particular the importance and meaning of uncovered colliders, may want to skip this section. Other readers may want to read it selectively to remind or refresh themselves on the material.
A node is a visual representation of a variable. The terms "node" and "variable" may be used interchangeably in this paper.
Like nodes and variables, edges can be both visual and abstract objects. Visually, edges are lines connecting nodes, sometimes augmented with arrowheads or other symbols along their length or at one or both endpoints. Abstractly, edges represent relationships between variables.
Two nodes are directly connected if there is an edge connecting them. An arrow is visually an edge with an arrowhead at one end and an arrowtail at the other, or abstractly a representation of an asymmetric relationship between two variables.
An undirected edge is an edge that is not augmented with any arrowheads. A graph is a pair, hF, Ci, where F is a set of nodes and C is a (possibly empty) set of edges among the nodes in F. For any graph G, let F G refer to the set of G's nodes and C G refer to the set of G's edges. Note that given the dual roles of all the terms used to define "graph", it can be both a visual and an abstract object. A partially directed graph is a graph that contains only directed and undirected edges. Note that there are many other kinds of edges that partially directed graphs exclude, such as bidirected edges (edges with arrowheads at both ends), and edges augmented in other ways such as colors, dashed lines, or other kinds of symbols at the endpoints. Unlike graphs that might contain those more enriched edge types, partially directed graphs only permit two types of edges: directed and undirected. Directed graphs and undirected graphs are special types of partially directed graphs.
The skeleton of a graph is the set of directly connected pairs of variables in the graph. Two graphs can have the same skeleton if the same nodes are directly connected, even though the edges between those nodes may be different. More formally, graph G = hF, Ci has skeleton S(G) = hF, C 0 i where C 0 = {(X, Y)|X and Y are directly connected in G}, with (X, Y) being the unordered pair of nodes X and Y.
A path in a graph G = hF, Ci is an alternating sequence of nodes in F and edges in C, such that each edge directly connects the node before it to the node after it in the sequence. Paths both start and end with nodes. Paths are typically described as being from one endpoint and to the other endpoint, e.g. "a path from A to B." An example of a path is hA, A ! B, Bi. For brevity, when stating a path I will drop the repeated characters from the edges, e.g. hA, !, Bi.
A directed path in a graph is a path such that all arrows point in the same direction, e.g. hA, !, B, !, C, !, Di. Directed paths have one node with no arrows pointed into it on the path, called the source, and one node with no arrows pointing out of it on the path, called the sink. Directed paths are typically described as going from the source and to the sink.
A directed acyclic graph (DAG) is a directed graph that contains no directed paths with multiple instances of the same node. Intuitively, this means that one cannot return to the same node in a graph if one follows the arrows in the graph.

PLOS ONE
A collider is any 3-node path of the form hA, !, B, , Ci. The term collider may also be used to refer to the middle node of such a path, but never to the other two nodes. Any node which is not a collider on a path is sometimes called a noncollider.
An uncovered collider is a collider path such that the initial and terminal nodes are not directly connected in the graph that contains it. For example, let G be a graph, let A, B, and C be nodes in G, and let A ! B, and B C be edges in G. If A and C are not directly connected, then the path hA, !, B, , Ci is an uncovered collider, otherwise it is a covered collider.
A path P is active, or open, conditional on a set of nodes C, if none of the noncolliders along P are in C, and if every collider along P is either contained in C or has a descendent in C. Otherwise, the path is considered inactive, or closed, or blocked.
Nodes A and B are d-separated by C, or d-separated conditional on C, in a graph, if there is no active path between A and B conditional on C.
Two graphs are Markov equivalent if they entail precisely the same d-separation statements. Note that this is equivalent to having the same skeleton and uncovered colliders.
An essential graph is a partially directed graph representing a collection of Markov equivalent DAGs. Fig 1 provides an example of an essential graph. The term essential graph will be used to refer both to the partially directed graph and to the set of DAGs that it represents. As such, one may write that the DAG D is a member of, or within, an essential graph E.
A pattern is a complete Markov equivalence class of DAGs. An essential graph is not required to contain all graphs which are Markov equivalent to its members. Unlike patterns, essential graphs may be learned from observational data paired with background knowledge, or from experimental data, or from other forms of enriched data that allows the researcher to restrict the space of permissible DAGs. Since the pattern concept is a special case of the essential graph concept, the theorems in this paper that pertain to essential graphs naturally also apply to patterns.
A Dutch book can be made against an agent if there is a gamble that appears acceptable to the agent but for which a loss is guaranteed. In other words, the loss occurs regardless of the outcome of the random event upon which the gamble is made.
Dutch book arguments have historically been used to support the axioms of probability theory, by proving that (1) any person who does not gamble in accordance with these axioms can have a Dutch book made against them, while (2) any person who does gamble in accordance with these axioms is immune to Dutch books [22][23][24][25]. In this paper I use a similar argument form as previous Dutch book arguments, but applied to the interpretation of essential graphs rather than to the theory of probabilities. Proof. Let there be a DAG D over nodes A, B, C, and H. Let D be in essential graph G. Let G be such that there are undirected edges from each of A, B, and C, to H, and no other edges. In other words, G is a single hub network, with H being the hub. Fig 1 contains a

visualization of G.
Let Steve be an arbitrary scientist who assigns equal probability to both edge orientations of all undirected edges in essential graphs. I will offer Steve the following package of bets about the DAG D, in the context that we know it is a member of pattern G: Steve calculates his mean expected profit or loss, E, should he accept the first bet: Since Steve believes P(A ! H 2 C D |D 2 G) = 0.5, he calculates that E = 0.5 � 6 + 0.5 � -4 = 1. As such, Steve believes that this bet will on average profit him $1.
Following the same reasoning with the second and third bets, Steve comes to the same conclusion: he expects each bet to profit him $1. He calculates his expected profit for the package of all 3 bets as the sum of the expected profits of the individual bets, yielding $3. Since Steve expects to make money on average, he believes it is rational to accept the package of bets.
Steve's expectation is wrong, however, as he is in fact guaranteed to lose money upon accepting this package of bets.
To see why, note that G is an equivalence class of DAGs with 4 members. Fig 2 contains visualizations of all 4 DAGs in G. Note that in no member of G does H have more than one incoming edge. This is because: for any DAG that has the same skeleton as G, if H has 2 parents, then that would form an uncovered collider, implying different d-separation relationships among the variables than those found in G. This creates a dependence among the orientations of the edges in G: if we learn that A ! H, for example, then we can infer H ! B and H ! C, since that is the only graph in the equivalence class that includes A ! H.
As such, Steve can only win at most one of the 3 bets in the package, and is guaranteed to lose at least 2. The best case scenario for Steve is a net profit of $6 + $4 + $-4 = $-2, or a loss of $2. By accepting this package of bets, Steve is guaranteed to lose at least $2, for all possible D 2 G. Thus we have made a Dutch book against Steve.
The main idea behind this Dutch book is to construct an essential graph where assigning equal probability to both edge orientations in all unoriented edges is not consistent with any assignment of probabilities to the DAGs in that essential graph. Not all essential graphs are like this, and assigning equal probabilities to edge orientations does not not enable the construction of Dutch books for all essential graphs. For example, if the essential graph in question is A -B, a graph with only two nodes and one undirected edge, then any assignment of probabilities to A ! B and B ! A that sums to 1 cannot be Dutch booked.
For essential graphs like G in Fig 1, however, no assignment of probabilities to the four DAGs in that equivalence class, shown in Fig 2, can produce equal edge orientation probabilities for all three unoriented edges. Once this incoherence is established, any number of Dutch books can be constructed.
G is not the only type of essential graph with this property, and further, this situation cannot be resolved by adding additional nodes that connect to the 4 nodes in G (although it can be resolved by adding or removing edges in G). Essential graphs like G for which equal edge orientation probabilities are incoherent are not rare.

Avoiding dutch books
A more complicated strategy for accepting gambles about edge orientations is immune to Dutch books of this kind. Informally, instead of the agent assigning edge orientation probabilities to individual edges without considering their larger context, this strategy begins by considering the entire set of DAGs in the equivalence class, creating a probability distribution over this set of DAGs. Individual edge orientation probabilities can then be computed from the probabilities of the DAGs.
In order to both state and prove the theorem, we need a few more definitions. Let I O (A, B, D) be the indicator function for determining whether A ! B is in DAG D: Let an edge orientation bet (EOB) be a 5-tuple C = hG, A, B, R, Li encoding the following bet. There is an unoriented edge between variables A and B in an essential graph G, for which a DAG D 2 G will be revealed. An agent who accepts the bet has a change of (A, B, D)).
Let a collection of edge orientation bets (CEOB) be a nonempty set of EOBs that all refer to the same essential graph G, and for which the same DAG D 2 G will eventually be revealed, but is initially unknown. An agent who accepts a CEOB accepts all EOBs within it, which may refer to different undirected edges in G.

Theorem 2. If an agent's credences correspond to a probability distribution P G over all DAGs D 2 G, and the agent only accepts a CEOB E if
Since the agent accepts no other CEOB, then any CEOB which might form a Dutch book against the agent must satisfy that condition. Let E be such a CEOB, so: Since both sums are finite, we can reorder them, giving: If 8 D2G ∑ C2E δ C (D) = 0, then it follows immediately that ¬8 D2G ∑ C2E δ C (D) < 0, i.e. E is not a Dutch book. So, let us assume instead: 9 D2G ∑ C2E δ C (D) 6 ¼ 0. Since the sum over D 2 G is � 0, and one element of this sum is non-zero, then at least one of the elements of the sum must be > 0. This means: Since P G (D) does not depend on C, we can move it outside the sum. Since P G (D) is positive, we can multiply both sides by 1/P G (D) without changing the inequality, leaving us with: Introducing two negations outside of this statement, and then passing the inner negation all the way through to the inequality, gives us the following:

PLOS ONE
We can immediately infer the weaker statement: This final line explicitly negates that E is a Dutch book, concluding the proof.
Put plainly, this theorem states that agents who assign a coherent probability distribution to the DAGs in G, and only accept a gamble when their expected value for that gamble is at least zero, cannot be Dutch booked when the gamble pertains strictly to outcomes of various edge orientations.

Conjecture
Theorem 1 showed that it is possible to be Dutch booked when one's beliefs about edge orientations don't align with the set of DAGs in the essential graph, and theorem 2 showed that those Dutch books can be avoided by forming beliefs about the set of DAGs prior to forming beliefs about edge orientations. It remains to be shown that only those agents who form beliefs in accordance with theorem 2 can avoid those Dutch books.
Since we are now considering that agents may form probabilistic beliefs regarding individual edge orientations, rather than entire DAGs, we need to add some additional notation. Let P α (A, B) be agent α's probabilistic beliefs about whether edge A ! B will be in the revealed DAG D 2 G. Let δ C (A, B) be the change in value to an agent who accepts bet C after it is found that the edge A ! B is in the revealed DAG D 2 G. Conjecture 1. Given an essential graph G, if an agent α has beliefs P α (A, B) that are not consistent with any P G over all DAGs D 2 G, i.e. if Should this conjecture be proven, it would imply that the sufficient condition for avoiding CEOB Dutch books defined in Theorem 2 is also a necessary condition for avoiding CEOB Dutch books. I have attempted to prove this conjecture, but found progress difficult due at least in part to there being many different ways in which some P α may not be consistent with any P G , and due to the large space of possible CEOBs which one might try exploit to construct a Dutch book. Different such P α 's might require very different CEOBs in order to be Dutch booked, and I found it difficult to characterize all such interactions.

Prevalence of imbalanced undirected edges
It is clear that there exist undirected edges which, assuming a uniform distribution over the DAGs in the essential graph, do not have a 50% chance of being oriented in either direction. Such edges might be rare, though. How likely is it that any particular undirected edge we encounter is a 50% edge versus an edge biased towards one direction or the other?
The likelihood of 50% edges is greatly dependent on graph properties such as the ratio of edges to nodes in the graph. In completely connected patterns, for example, all undirected edges are 50% edges. Similarly, in very sparse graphs where all edges are isolated from each other, preventing paths of length 2 or greater from forming, all undirected edges are also 50% edges. The more interesting graphs are those in the middle ground, with enough edges to form longer paths but not to form large heavily connected subgraphs. There are a total of 152 DAGs with 4 variables and 3 edges. There are ð 6 3 Þ ¼ 20 skeletons, each with 2 3 = 8 ways of orienting the 3 edges, for a total of 160 directed graphs. Of these, 8 contain directed cycles (2 for each skeleton with 3 fully connected nodes), and are thus not DAGs. In total, (96 + 48)/(96 + 48 + 48 + 48 + 72) � 46% of the unoriented edges are 75% oriented in one direction. While this example only concerns graphs with 4 edges and 3 nodes, it indicates that undirected edges strongly biased towards one direction may be common. , their corresponding essential graphs, the DAGs contained in those essential graphs, the total number of DAGs that fall into each type of essential graph, and the quantity of undirected edges with equal and unequal odds of being oriented in either direction, given a uniform distribution over the DAGs. In addition to undirected hub graphs, undirected chain graphs also contain undirected edges which are more likely to be oriented in one direction. With 4 nodes and 3 edges, other than 50% edges there are only 75% edges and completely directed edges. Overall, 46% of the undirected edges are 75% edges, indicating that non-50% edges are common among graphs with 4 nodes and 3 edges. https://doi.org/10.1371/journal.pone.0249415.g003

Discussion
An essential graph consisting of a single undirected edge is safe from Dutch books no matter how the agent assigns probabilities to that edge's orientations (assuming they still abide by the basic rules of probability, of course). While ignoring the inter-dependencies of orientations of undirected edges does not guarantee that an agent will be vulnerable to a Dutch book, it does leave open that possibility. The "50% either way for all unoriented edges" rule makes an agent vulnerable to Dutch books, but not for all essential graphs. It is still enough to be disatisfied, since an alternative is available.
These theorems have implications for at least a few real world statistical settings. First, a domain expert who has used a pattern-learning statistical procedure to estimate the Markov equivalence class of causal DAGs will often wonder how likely it is for the undirected edges to be oriented one way or another. It is unsatisfying, and perhaps even psychologically impossible, to have no credence whatsoever for these graph features. Treating the odds of two possible outcomes as a coin toss is simple and intuitive, when one lacks any information suggesting one is more likely than the other. It is thus plausible that numerous researchers who have used causal discovery software are vulnerable to a Dutch book. Exposure to the theorems contained in this paper might make these researchers more careful when interpreting undirected edges in patterns.
Second, there is some uncertainty regarding how to treat undirected edges in ensembles of essential graphs. One procedure is to, for each pair of variables, report the proportion of times each of the possible edge orientations occurred [15]. The theorems in this paper suggest, however, that we ought not to interpret undirected edges without considering their context, i.e. the essential graph that they occur within. This makes interpretation of the frequency of undirected edges in a table of edge orientation frequencies difficult, as such a table removes any such notion of graphical context of individual edges. It is possible that the count of undirected edge orientations is summing over what is actually a highly heterogeneous collection of edges, despite them all being undirected.
A third point follows from the other two. When considering how "correct" an ensemble of essential graphs is relative to a target DAG, there is some question as to how to treat the learned undirected edges. Without knowing the rest of the graph that these undirected edges are part of, many of those undirected edges may come from patterns in which they are much more likely to be oriented in one direction or the other, assuming a uniform distribution over the Markov equivalent DAGs.

Conclusion
In this paper I've stated and proven two theorems about the interpretation of essential graphs. The first theorem states that a simple interpretation of undirected edges, namely giving equal credence to both orientations of all such edges, leads to a Dutch book. The second theorem states that a more complex interpretation, which takes the entire graph into account rather than attempting to interpret edges independently of each other, is immune to such a Dutch book. I conjectured but did not prove that any interpretation that is not consistent with this one will be vulnerable to a Dutch book. In other words, I proved that this interpretation is sufficient for avoiding a Dutch book, and conjectured that it is also necessary. Finally, this paper also demonstrates that undirected edges which are more likely to be oriented in one direction than another may be quite common.
This paper focuses on essential graphs, which are a simple and widespread graphical representation of Markov Equivalence Classes of DAGs, but essential graphs have representational limitations. For example, essential graphs cannot represent the possibility of an unmeasured common cause of two or more measured variables. Other types of graphs, such as Mixed Ancestral Graphs, are capable of representing these more complex graphical structures and their corresponding Markov Equivalence Classes. Future work should extend the ideas in this paper to these more expressive but more complicated graphs.
Theorem 2 demonstrates that considering the entire set of graphs in the Markov Equivalence Class can protect us from being Dutch booked, but this may be difficult to implement in practice. This difficulty is primarily computational: Markov Equivalence Classes can contain large numbers of DAGs. These sets can be so large that enumerating all their members, let alone reasoning from all of them simultaneously, is prohibitively computationally expensive. In many graphs, though, one would not actually need to consider the entire set of DAGs in order to determine safe credences about edge orientations. For example, a graph could have two completely disconnected subgraphs, in which case the edges in either subgraph would not be relevant to determining credences about the edge orientations of the other subgraph. Previous work has developed computationally efficient ways of estimating total causal effects for all DAGs in an equivalence class [26]. Similar methods should be explored in order to develop computationally efficient procedures for computing safe credences about edge orientation probabilities.


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Computer Science
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Application and Prospect of Blockchain Technology in China's Commercial Banks

For the traditional financial industry, blockchain technology has brought an unprecedented revolution. In recent years, blockchain technology has become one of the most important research directions of financial technology in commercial banks in China. The application of blockchain technology in commercial banks can help optimize payment and settlement system, improve credit risk management and control mode, improve the operation efficiency of the middle and back office, and develop smart finance. At the same time, blockchain technology brings many challenges. Based on the introduction of the evolution and characteristics of blockchain, this paper analyzes the necessity of the application of blockchain technology in China's commercial banks, puts forward the policy advices of the application of blockchain in commercial banks.

INTRODUCTION
To solve the "Byzantine general problem" and "double spending problem" is the real root of the core idea of blockchain technology. Blockchain is the most subversive and shining technological invention in the field of financial network in the past 10 years. In the foreseeable future, it will reshape the entire financial ecosystem.
As we all know, trust is the foundation of the transfer, transaction, storage and payment of value in the world. Without trust, human beings will not be able to complete any value exchange. Originally, people relied on blood and religion to build trust. With the progress of people's cognition of social, moral restraint and religious belief became the basis of trust building among people. Then, the progress of science and technology created a strong demand for rule consciousness. Through building trust by laws and organizations, different kinds of value transfer and exchange are completed. In the Internet era, people begin to take "self-media" and "self-finance" as authority instead of the information center of traditional society. Every single person has become an information center and human society therefore enters the "multicentric era". Based on the above, blockchain technology decentralize with a pure mathematical method, establish a strong connected trust relationship between the weak relations between the two sides of the transaction, so as to complete a kind of value exchange with low cost [1]. By continuing and enlarging the decentralization trend of Internet technology, blockchain technology has been highly concerned and recognized by various industries, and has become the major field of research and test direction in the new round of technological innovation. As the basic supporting technology of Internet finance, Internet of Things finance and big data application, blockchain technology has the characteristics of decentralization, tamperability, traceability and programmable intelligent contract, which makes it have better commercial application value in digital currency, financial transaction, asset registration and right confirmation.
Blockchain, with its core competitive advantages of high performance, high security, high-speed access and efficient operation, has attracted extensive attention from all sectors of society. With the rapid development of blockchain, China complies with the needs of globalization, keeps up with the pace of international development, and actively promotes the development of blockchain related research, standardization and industrialization.

THE NECESSITY OF APPLYING BLOCK CHAIN TECHNOLOGY TO CHINA'S COMMERCIAL BANKS
According to the survey, most of the Internet finance in China's commercial Banks at the present stage is based on the traditional electronic, and the transactions must be built on the basis of mutual trust still. To be specific, especially in China, which is a traditional trustbased country, the business model is also based on the social organization structure [2]. The premise of the transaction between the two sides is to have a trust system, which can be completed by the government supervision department, bank credit system and other institutions. Without this kind of trust system, unfamiliar parties will not trade. However, blockchain technology can completely change this traditional mode. Due to the characteristics of decentralization and autonomy, blockchain technology can establish a trust system in each blockchain by establishing a set of trust-based electronic algorithm. So, both parties of transaction can carry out economic activities without trust system, which reduces enterprise costs, achieves the lowest value transfer and gains higher profit. It can be said that blockchain technology is a creative revolution in the history of human social credit, which is very likely to reconstruct human production and life style. At present, the in-depth study and application of blockchain technology will have a positive and far-reaching impact on the payment and settlement, risk prevention and control, IT architecture and other aspects of China's commercial banks.

Rebuild and upgrade payment and settlement system of commercial Banks
At present, the payment and settlement of commercial banks need firstly to go through the payment and settlement center of the people's Bank of China, and then through the complicated process of payment and settlement instruction initiation, information feedback, payment and settlement bookkeeping, bilateral transactions and balance reconciliation. The whole process takes a long time and costs a lot. If joining the blockchain alliance, commercial banks can realize the direct payment and settlement of both parties through the blockchain system instead of the payment and settlement center of the people's Bank of China, with higher efficiency, better experience and lower cost.

Innovate and improve credit risk management and control mode for commercial banks
At present, the risk control model of commercial banks mainly relies on the data of credit investigation center of the people's Bank of China, cooperation data of relevant government departments, data of domestic credit investigation institutions, third-party payment and settlement and e-commerce data. With the development of blockchain technology, transaction data of various enterprises and individuals, especially financial transaction data, will be stored in the blockchain system. Because every block node in the blockchain system can automatically copy and share all the information data, commercial banks can obtain all kinds of transaction data by joining the blockchain alliance, which overcome the adverse factors such as information asymmetry, incomplete information, untimely information update and high information transaction cost.

Improve the operation efficiency of the middle and back office of commercial banks
The emergence of blockchain technology makes it possible to process financial transactions in real time. At present, the middle and back offices of commercial Banks need a large number of personnel for the registration and settlement of financial transactions. With the help of multiple signature technology, unique recording technology and intelligent technology contract, blockchain technology will promote the real-time and automatic processing of financial transactions, bookkeeping, settlement and other work, which will avoid repeated financial transaction processing and false transaction processing, improve the accurate processing of trigger conditions in multiple transaction subjects' transaction projects by commercial banks and improve the efficiency of collaborative work.

Develop intelligent finance and Internet of things finance of commercial banks
The important role of blockchain technology for Internet of things finance lies in not only building a comprehensive and low-cost communication bridge between Internet of things information system and many intelligent devices, but also improving the reliability and privacy of Internet of things information system through decentralization, reliability and consensus mechanism [3]. The important significance of blockchain technology for intelligent finance is that all kinds of financial intelligent devices can store financial information in the blockchain system, which is managed uniformly by the blockchain system and automatically copied and shared by each node of the blockchain, thus greatly improving the coordination of financial intelligent devices, promoting the interconnection of financial intelligent devices and automatic management according to the intelligent contract, and realizing the self-sufficiency of intelligent devices Governance.

Social understanding constraints
From the perspective of the overall technical environment, all sectors of society still hold a cautious and even negative attitude towards blockchain technology. On the one hand, due to the lack of professional knowledge of blockchain and the news of bitcoin "roller coaster", it is easy for the outside world to equate blockchain with encrypted digital currency. On the other hand, there is a phenomenon of overstating the function of blockchain, and it is believed that blockchain will subvert all the existing Internet. These are all biased views. In terms of technology itself, blockchain is the supplement and innovation of Internet technology application level, but it is not subversion.

Technical constraints
As a new technology, blockchain has some problems, such as lack of practical data, unknown market tolerance, and market education needs a certain time. As a blockchain structure, all information participants in the blockchain have a "public information account book" to ensure that the information can not be tampered with and traceable. With the development of time, there are more and more nodes, which will lead to the increasingly serious problem of redundant storage, the dramatic increase of cost and directly lead to the problems of too long blockchain structure, slow data update and response. In addition to the performance may not be able to meet the high-frequency transactions, blockchain technology is still immature, and there are certain security risks, including whether the algorithm is reasonable, whether there is the possibility of cracking. In the application of smart contracts, once an error occurs, it may cause a "Butterfly Effect", thus causing major data security accidents. Therefore, it will take a long time for blockchain technology to fully enter the commercial application field. For commercial banks, the strict technical requirements will raise the entry threshold of blockchain and restrict the recognition and acceptance of blockchain finance in the banking industry [4].

Cost constraints
The development of blockchain finance in the banking industry will consume huge human and material resources. For banks, financial innovation should not only ensure economic benefits, but also comply with regulatory requirements. At present, blockchain technology is in the research and development period, and the relevant technology and use standards are not unified. The current infrastructure of financial institutions has spent a lot of manpower and material resources after years of construction and maintenance, which is relatively perfect. The use of blockchain system will completely subvert this traditional mode and create a new transaction settlement and management system. In this process, the connection between the existing system and the blockchain system will cost a lot. At the same time, various obstacles from the bank will slow down the promotion of blockchain finance in the field of commercial banks.

Legal and institutional constraints
Although the decentralized supervision mode of blockchain technology will solve the problems of high supervision cost and low efficiency that commercial banks have been facing, its credit creation form will weaken the existing institutional advantages of the country [5]. Taking bitcoin as an example, it not only threatens the authority of the central bank to issue money, but also affects the monetary transmission mechanism and effect, and weakens the country's macrocontrol ability. The central bank has to be cautious about this. At present, China's legal establishment in this area is relatively lagging behind, and the mode of policy supervision is not clear.

RESPONSE STRATEGIES OF COMMERCIAL BANKS TO BLOCKCHAIN TECHNOLOGY
As a new technology, blockchain technology is still in the exploratory stage, there are many uncertainties, but in the core advantages of information co construction and sharing, decentralization, smart contract, credit creation, etc., it is bound to have a huge impact on the service means and operation mode of traditional commercial banks, and bring challenges and opportunities to banks. Commercial banks can strengthen the application of blockchain technology from the following points.

Formulate and improve corresponding laws and blockchain application standards
At present, many countries and regions at home and abroad have set foot in blockchain, and local governments have also issued relevant policies to support and encourage the development of blockchain, but there is still a lack of policy documents related to overall planning and top-level design in China. The legal and regulatory departments should start from the toplevel design, based on the national development strategy, plan the application of blockchain technology in the field of commercial banks, issue corresponding industrial policies, and guide the orderly development of blockchain technology.
In addition, in view of the existing problems in the development of blockchain, such as technical heterogeneity and inconsistent standards, domestic commercial banks should strengthen cooperation, establish a competitive blockchain alliance, actively participate in the formulation of industry standards, refine the industry application standards in line with their own development, and strive for more voice. In 2016, the first blockchain alliance in China -distributed general ledger basic protocol alliance was established. The alliance is launched in the existing commercial network to realize the real name of digital identity between nodes, which greatly improves the efficiency, but this is only the beginning. Only by formulating the application standards of blockchain in line with China's national conditions, and promoting the development of domestic commercial banks, is the ultimate goal.

Strengthen talent reserve, change research and development ideas and application positioning
The competition of technology is actually the competition of talents. In this tide of blockchain development, if we want to catch up with and surpass, domestic commercial banks need to strengthen the absorption and reserve of blockchain technical talents, establish blockchain innovation laboratories within or between banks, and break the technical barriers [6]. In the research and development process of blockchain technology, we should take the stability of the economic and financial system as the starting point and end point, and change the application orientation of blockchain technology from "decentralization" to "value interconnection, information co construction and sharing". Based on this positioning, in the practical application process, it can not only avoid the link between virtual currency and existing legal currency, ensure the stable and healthy operation of the financial system, but also reduce the impact of blockchain technology on the existing monetary and economic system, participate in domestic and international banking cooperation with an open mind, and rationally promote the application of blockchain technology.

Conduct multi-directional data information management and control to enhance the safety and reliability of network system
Undoubtedly, the core of "decentralization" of blockchain technology is actually the distributed storage and dynamic update of data. With the increasing degree of decentralization and the number of participating nodes, redundant data storage becomes difficult. This requires finding a proper balance between redundant data storage and decentralization, system response speed and customer satisfaction. Therefore, commercial banks need to strengthen investment in infrastructure and R & D, as well as cooperation among different departments such as business, operation, risk control, it, etc., focus on encryption algorithm, data collection and storage, carry out comprehensive security control and dynamic tracking, and improve the ability of multi-party data information

CONCLUSION
The characteristics of the blockchain of business management of commercial banks are process simplification, storage optimization, cost reduction and so on. It has many intersections with the business of commercial banks. However, there will be many difficulties before the implementation of blockchain technology, such as improving transaction speed, privacy protection, confirmation process, data capacity limitation, etc. Blockchain has the nature of network sharing. In the large-scale application of blockchain technology, there are some phenomena such as improper protection of privacy information, illegal embezzlement of core data, etc. Therefore, we should pay attention to the development potential of blockchain technology. Blockchain technology continues the Internet technology. With the win-win cooperation of technology companies, private equity funds, venture capital funds and other markets, we will tap the commercial value of blockchain, promote more institutions to participate in blockchain technology, better organize and layout blockchain, and change the way of financial transactions.
At the same time, the extensive application of new technologies needs to experience the test of practice. Although blockchain and commercial banking business have certain adaptability, they still have certain obstacles in the combination. The technology of blockchain itself has also been constantly improved in trials, and its development cannot be achieved overnight. In the future, it still needs constant innovation and active cooperation and communication from all market players to promote greater breakthrough and development of blockchain application.